CREDITS:All corresponding resources
MOTIVATION:Motivation to create this repository to help upcoming aspirants and help to others in the data science field
https://www.theinsaneapp.com/2021/03/how-to-build-machine-learning-project.html
**** If you like my work. please buy me a coffee it motivate me -> https://www.buymeacoffee.com/achuthasubhash?new=1 ****
Business understanding
1.Data collection
Data consists of 3 kinds
a.Structure data (tabular data,etc...)
b.Unstructured data (images,text,audio,etc...)
c.semi structured data (XML,JSON,etc...)
variable
a.qualitative (nominal,ordinal,binary)
b.quantitative(discrete,continuous)
https://www.chi2innovations.com/blog/discover-data-blog-series/data-types-101/
database scraping data from websites purchasing data data from surveys data, sensors, cameras, apis etc.
cleanlab https://l7.curtisnorthcutt.com/cleanlab-python-package https://github.com/cgnorthcutt/cleanlab https://github.com/cgnorthcutt/label-errors https://github.com/cgnorthcutt/rankpruning https://github.com/subeeshvasu/Awesome-Learning-with-Label-Noise
Measure Data Quality ydata-quality https://github.com/ydataai/ydata-synthetic https://towardsdatascience.com/how-can-i-measure-data-quality-9d31acfeb969
a.Web scraping best article to refer-https://towardsdatascience.com/choose-the-best-python-web-scraping-library-for-your-application-91a68bc81c4f
https://www.kdnuggets.com/2021/02/6-web-scraping-tools.html
https://www.bigdatanews.datasciencecentral.com/profiles/blogs/top-30-free-web-scraping-software
https://towardsdatascience.com/6-web-scraping-tools-that-make-collecting-data-a-breeze-457c44e4411d
https://medium.com/analytics-vidhya/master-web-scraping-completly-from-zero-to-hero-38051423256b
1.Beautifulsoup https://www.freecodecamp.org/news/how-to-scrape-websites-with-python-and-beautifulsoup-5946935d93fe/
mechanicalsoup https://analyticsindiamag.com/mechanicalsoup-web-scraping-custom-dataset-tutorial/
2.Scrapy,PyScrappy,Pandas Datareader,Instaloader,lxml
3.Selenium https://www.freecodecamp.org/news/better-web-scraping-in-python-with-selenium-beautiful-soup-and-pandas-d6390592e251/
4.Request to access data
5.AUTOSCRAPER - https://github.com/alirezamika/autoscraper https://www.youtube.com/watch?v=9BQ353Yu1D0 https://www.analyticsvidhya.com/blog/2021/04/automate-web-scraping-using-python-autoscraper-library/
scrapeasy Scrape Any Website in Seconds with One Line of Code https://github.com/joelbarmettlerUZH/Scrapeasy
Scrap Images From E-Commerce Website Using AutoScraper https://www.analyticsvidhya.com/blog/2021/05/scrap-images-from-e-commerce-website-using-autoscraper-library/
amazon auto scraper library https://webautomation.io/
Listly https://www.listly.io/r/stdfr
FiftyOne Now easier to download and evaluate https://towardsdatascience.com/googles-open-images-now-easier-to-download-and-evaluate-with-fiftyone-615ce0482c02
webbot https://pypi.org/project/webbot/
gazpacho https://github.com/maxhumber/gazpacho
html_scraper_streamlit_app https://www.youtube.com/watch?v=6U5xJ3mXRKA&feature=youtu.be
6.Twitter scraping tool (𝚝𝚠𝚒𝚗𝚝 or tweepy or tweetlib)-https://github.com/twintproject/twint
twitterscraper https://www.youtube.com/watch?v=MpIi4HtCiVk
twython https://github.com/ryanmcgrath/twython
twarc https://github.com/DocNow/twarc https://scholarslab.github.io/learn-twarc/01-quick-start.html
snscrape extract twitterr data https://github.com/JustAnotherArchivist/snscrape
Scweet A simple and unlimited twitter scraper https://github.com/Altimis/Scweet
GetOldTweets3,GoogleNews,snscrape,GetOldTweets3
Scrape Twitter for Tweets https://github.com/taspinar/twitterscraper
HAR File Web Scraper https://stevesie.com/har-file-web-scraper https://www.youtube.com/watch?v=LcqVDfueb8g
https://analyticsindiamag.com/complete-tutorial-on-twint-twitter-scraping-without-twitters-api/
https://developer.twitter.com/en/docs
pytrends https://medium.com/nerd-for-tech/scraping-data-from-online-platforms-to-enhance-time-series-forecasts-6eec3c68636d
Scraping Instagram -instaloader https://thecleverprogrammer.com/2020/07/30/scraping-instagram-with-python/
Instascrape
Scrape LinkedIn Profiles with ProxyCurl API
Reddit Dataset Using PSAW and PRAW in Python
Scraping Reddit using Python Reddit API Wrapper (PRAW)
Scrape Wikipedia wikipedia https://www.thepythoncode.com/article/access-wikipedia-python
patang - Scrape Product details from eCommerce Sites with Puppeteer and DOM String https://www.youtube.com/watch?v=3sgxRmyOuXs
Download Wikipedia https://www.wikidata.org/wiki/Wikidata:Main_Page https://www.youtube.com/watch?v=hC1rY4lRY0s https://towardsdatascience.com/an-efficient-way-to-read-data-from-the-web-directly-into-python-a526a0b4f4cb
Web Scraping to Create a CSV File https://thecleverprogrammer.com/2020/08/08/web-scraping-to-create-csv/
Amazon Web Scraper, Amazon Auto Scraper
7.urllib
8.pattern
9.Octoparse Easy Web Scraping https://www.octoparse.com/
prowebscraper https://prowebscraper.com/features
Web scraper https://chrome.google.com/webstore/detail/web-scraper-free-web-scra/jnhgnonknehpejjnehehllkliplmbmhn?hl=en
ParseHub https://www.parsehub.com/ https://analyticsindiamag.com/parsehub-no-code-gui-based-web-scraping-tool/
PyScrappy https://github.com/mldsveda/PyScrappy https://www.analyticsvidhya.com/blog/2022/02/web-scraping-with-pyscrappy/
Gazpacho https://github.com/maxhumber/gazpacho
ScrapeSimple Website: https://www.scrapesimple.com
Content Grabber https://contentgrabber.com/Manual/understanding_the_concept.htm
Crawly https://crawly.diffbot.com/
Apify https://apify.com/
Mozenda Website: https://www.mozenda.com/
obsei https://github.com/lalitpagaria/obsei
Diffbot https://analyticsindiamag.com/diffbot/
Trustpilot,webhose,scrapingbot
lxml https://lxml.de/index.html#introduction
ScrapingBee https://analyticsindiamag.com/scrapingbee-api/
Scrape HTML tables https://www.youtube.com/watch?v=6U5xJ3mXRKA&feature=youtu.be or pd.read_html
requests-html https://github.com/kennethreitz/requests-html
newspaper https://github.com/codelucas/newspaper https://www.youtube.com/watch?v=Hfry5XnISyc
newspaper3k: https://newspaper.readthedocs.io # easily extract text from articles
newscatcher https://github.com/kotartemiy/newscatcher https://www.youtube.com/watch?v=pHzOuizZq4I
patang (extract product details) https://github.com/tejazz/patang
lisc https://github.com/lisc-tools/lisc
Helena WEB AUTOMATION FOR END USERS https://helena-lang.org/
pandas(read_html)
wget,curl,parsehub,webhouse,octoparse,scraping bot,scraping bee,Common,Content Grabber,Docparser,Scraper API,Import.io,Altair Monarch,WebAutomation.io,WebScraper.io,Scrape.do, AvesAPI, ParseHub, Import.io, Octoparse, Scrapingdog, Diffbot, ScrapingBee, Grepsr, Scraper API, Scrapy
Crawl Crawly https://crawly.diffbot.com/
HTML basics for web scraping,Web Scraping with Octoparse,Web Scraping with Selenium
10-best-web-scraping-tools https://www.scraperapi.com/blog/the-10-best-web-scraping-tools/
https://www.kdnuggets.com/2021/02/6-web-scraping-tools.html
https://analyticsindiamag.com/complete-learning-path-to-web-scraping-with-all-major-tools/ https://towardsdatascience.com/6-web-scraping-tools-that-make-collecting-data-a-breeze-457c44e4411d
https://towardsdatascience.com/6-web-scraping-tools-that-make-collecting-data-a-breeze-457c44e4411d https://www.kdnuggets.com/2018/02/web-scraping-tutorial-python.html
https://www.octoparse.com/ https://github.com/tirthajyoti/pydbgen https://www.mozenda.com/ https://www.mockaroo.com/ https://lionbridge.ai/ https://www.mturk.com/ https://appen.com/
11.GoogleImageCrawler,google_images_download,bing_image
https://www.freepik.com/popular-photos , https://stocksnap.io/ , https://www.pexels.com/ ,https://unsplash.com/ , https://pixabay.com/
b.Web Crawling
https://python.libhunt.com/scrapy-alternatives
Flat Data https://octo.github.com/projects/flat-data
b.3rd party API'S
22 APIs every data scientist should learn https://www.springboard.com/library/data-science/top-apis-for-data-scientists/
c.creating own data (manual collection eg:google docx,servey,etc...) primary data
d.etl awesome ETL https://github.com/pawl/awesome-etl#python https://github.com/achuthasubhash/awesome-etl
38x faster data pipelines with tf.data
d.Databases
Databases are 2 kind sequel and no sequel database
sql,sql lite,mysql,mongodb,montydb,hadoop,elastic search,cassendra,amazon s3,hive,googlebigtable,AWS DynamoDB,HBase,oracle db
sql https://mode.com/sql-tutorial/ https://www.w3schools.com/sql/
sql in python https://medium.com/jbennetcodes/how-to-rewrite-your-sql-queries-in-pandas-and-more-149d341fc53e
PyMongo https://analyticsindiamag.com/guide-to-pymongo-a-python-wrapper-for-mongodb/
Cloud AI Data labeling service https://cloud.google.com/ai-platform/data-labeling/docs?utm_source=youtube&utm_medium=Unpaidsocial&utm_campaign=guo-20200503-Data-Labeling
e.Online resources - ultimate resource https://datasetsearch.research.google.com/ https://medium.com/swlh/where-to-find-awesome-machine-learning-datasets-6bb909a3f350
10 BEST DATA COLLECTION TOOLS FOR EFFECTIVE RESULTS https://www.analyticsinsight.net/10-best-data-collection-tools-for-effective-results/
https://www.freecodecamp.org/news/https-medium-freecodecamp-org-best-free-open-data-sources-anyone-can-use-a65b514b0f2d/ https://research.google/tools/datasets/
Machine learning datasets https://www.datasetlist.com/ https://wiki.pathmind.com/open-datasets
https://guides.library.cmu.edu/az.php https://docs.microsoft.com/en-us/azure/azure-sql/public-data-sets https://registry.opendata.aws/ https://paperswithcode.com/datasets https://datasets.quantumstat.com/ https://www.quandl.com/ http://dataportals.org/ https://opendatamonitor.eu/frontend/web/index.php?r=dashboard%2Findex https://en.wikipedia.org/wiki/List_of_datasets_for_machine-learning_research https://www.quora.com/Where-can-I-find-large-datasets-open-to-the-public https://www.reddit.com/r/datasets/ https://ourworldindata.org/ https://data.worldbank.org/ https://data.world/ https://data.census.gov/cedsci/ https://data.seattle.gov/ https://www.openml.org/ https://visualdata.io/discovery
World’s Largest Data Platform https://worlddata.ai/
Awesome list of datasets in 100+ categories https://www.kdnuggets.com/2021/05/awesome-list-datasets.html
https://sebastianraschka.com/blog/2021/ml-dl-datasets.html https://enoumen.com/2021/04/23/data-sciences-datasets-data-visualization-data-analytics-big-data-data-lakes/
https://serokell.io/blog/best-machine-learning-datasets https://medium.com/@ODSC/25-excellent-machine-learning-open-datasets-940ca2124dfc
1)kaggle-https://www.kaggle.com/datasets , 𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚔𝚊𝚐𝚐𝚕𝚎𝚍𝚊𝚝𝚊𝚜𝚎𝚝𝚜
Downloading Kaggle datasets directly into Google Colab -https://towardsdatascience.com/downloading-kaggle-datasets-directly-into-google-colab-c8f0f407d73a
How to Download Kaggle Datasets using Jupyter Notebook https://www.analyticsvidhya.com/blog/2021/04/how-to-download-kaggle-datasets-using-jupyter-notebook/
2)https://sebastianraschka.com/blog/2021/ml-dl-datasets.html
movielens-https://grouplens.org/datasets/movielens/latest/
dagshub datset https://dagshub.com/explore/datasets
100+ of the Best Free Data Sources For Your Next Project https://www.columnfivemedia.com/100-best-free-data-sources-infographic/
World and national data, maps & rankings https://knoema.com/atlas/sources
3)data.gov-https://data.gov.in/
4)uci-https://archive.ics.uci.edu/ml/datasets.php https://github.com/tirthajyoti/UCI-ML-API
5)Group Lens dataset https://grouplens.org/
Wikipedia ML Datasets https://en.wikipedia.org/wiki/List_of_datasets_for_machine-learning_research
AWS Open Data Registry,data.gov (portals),YELP Open dataset,UNICEF Dataset,Big Bad NLP Database,Microsoft Dataset
6)world3bank https://data.world/ , worldbank
7)Google Cloud BigQuery public datasets
Google Public Datasets-cloud.google.com/bigquery/public-data/
Google Cloud Data Catalog https://cloud.google.com/data-catalog
Academic Torrents-https://academictorrents.com/check.htm?returnto=%2Fbrowse.php
8)online hacktons
Datasets https://www.paperswithcode.com/datasets
9)image data from google_images_download
https://www.visualdata.io/discovery
http://xviewdataset.org/#dataset
https://ai.googleblog.com/2016/09/introducing-open-images-dataset.html
10)image data from Bing_Search
image data from simple_image_download https://github.com/RiddlerQ/simple_image_download
11)https://www.columnfivemedia.com/100-best-free-data-sources-infographic
graviti Unleash the Power of Unstructured Data https://www.graviti.com/?utm_medium=0730Ismael
12)Reddit:https://lnkd.in/dv5UCD4 https://www.reddit.com/r/datasets/
praw.Reddit https://github.com/praw-dev/praw
13)https://datasets.bifrost.ai/?ref=producthunt
14)data.world:https://lnkd.in/gEK897K
15)https://data.world/datasets/open-data
https://tinyletter.com/data-is-plural
16)FiveThirtyEight :- https://lnkd.in/gyh-HDj , https://data.fivethirtyeight.com/
17)BuzzFeed :- https://lnkd.in/gzPWyHj
Buzzfeed News -github.com/BuzzFeedNews
Socrata - https://opendata.socrata.com/
18)Google public datasets :- https://lnkd.in/g5dH8qE
Statistics Canada https://www.statcan.gc.ca/eng/start https://towardsdatascience.com/how-to-collect-data-from-statistics-canada-using-python-db8a81ce6475
Deep Image Search AI-based image search engine https://github.com/TechyNilesh/DeepImageSearch
https://www.datasciencecentral.com/profiles/blogs/big-data-sets-available-for-free
19)Quandl :- https://www.quandl.com stock data
statista : https://www.statista.com/ stock data
20)socorateopendata :- https://lnkd.in/gea7JMz
21)AcedemicTorrents :- https://lnkd.in/g-Ur9Xy
22) Automates Image Annotation for Deep Learning Models https://medium.com/towards-artificial-intelligence/improving-data-labeling-efficiency-with-auto-labeling-uncertainty-estimates-and-active-learning-5848272365be
Label Studio,Sloth,LabelBox,TagTog,Amazon SageMaker GroundTruth,Playment,Superannotate,Playment,Dataturk,LightTag,Superannotate,CVAT,sloth,LabelImg,cvat
Automate data preparation https://www.superb-ai.com/
https://neptune.ai/blog/annotation-tool-comparison-deep-learning-data-annotation?utm_source=linkedin&utm_medium=post&utm_campaign=blog-annotation-tool-comparison-deep-learning-data-annotation
Diffgram,Label Studio ,CVAT,SuperAnnotate,Datasaur https://anthony-sarkis.medium.com/the-5-best-ai-data-annotation-platforms-for-machine-learning-2021-ec17c15142f3
https://foobar167.medium.com/open-source-free-software-for-image-segmentation-and-labeling-4b0332049878
***Label Assist: Model Assisted Pre-Annotation for Computer Vision https://blog.roboflow.com/announcing-label-assist/ https://www.youtube.com/watch?v=919CihTlkZw&feature=youtu.be***
https://github.com/jsbroks/awesome-dataset-tools
makeml https://makeml.app/
superannotate https://www.superannotate.com/
jupyter-innotater data annotator for Jupyter notebooks https://github.com/ideonate/jupyter-innotater
JupyterLab extension for annotating data https://github.com/explosion/jupyterlab-prodigy
semi-auto-image-annotation-tool https://github.com/virajmavani/semi-auto-image-annotation-tool
labelimage:- https://github.com/wkentaro/labelme , https://github.com/tzutalin/labelImg
labelCloud lightweight tool for labeling 3D bounding boxes in point clouds https://github.com/ch-sa/labelCloud
labeller https://www.labellerr.com/
prodigy Radically efficient machine teaching An annotation tool powered by active learning https://prodi.gy/
Labelbox-https://labelbox.com/
Playment-https://playment.io/
SuperAnnotate -https://www.superannotate.com/
CVAT-https://github.com/openvinotoolkit/cvat
Lionbridge- https://lionbridge.ai/
LinkedAI: A No-code Data Annotations- https://analyticsindiamag.com/linkedai/
Dataturks
V7 Darwin The Rapid Image Annotator https://docs.v7labs.com/docs/loading-a-dataset-in-python https://github.com/v7labs/darwin-py#usage-as-a-python-library
https://waliamrinal.medium.com/top-and-easy-to-use-open-source-image-labelling-tools-for-machine-learning-projects-ffd9d5af4a20
https://github.com/heartexlabs/awesome-data-labeling
Label a Dataset with a Few Lines of Code https://eric-landau.medium.com/label-a-dataset-with-a-few-lines-of-code-45c140ff119d
https://analyticsindiamag.com/complete-guide-to-data-labelling-tools/ https://neptune.ai/blog/data-labeling-software
Extraction of Objects In Images and Videos Using 5 Lines of Code https://towardsdatascience.com/extraction-of-objects-in-images-and-videos-using-5-lines-of-code-6a9e35677a31
https://neptune.ai/blog/data-labeling-software?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-data-labeling-software
23)tensorflow_datasets as tfds https://www.tensorflow.org/datasets (import tensorflow_datasets as tfds)
https://lionbridge.ai/datasets/tensorflow-datasets-machine-learning/
24)https://datasets.bifrost.ai/?ref=producthunt
25)https://ourworldindata.org/
26)https://data.worldbank.org/
27)google open images:https://storage.googleapis.com/openimages/web/download.html
30 Largest TensorFlow Datasets for Machine Learning https://lionbridge.ai/datasets/tensorflow-datasets-machine-learning/
https://cloud.google.com/bigquery/public-data/ https://towardsdatascience.com/bigquery-public-datasets-936e1c50e6bc
https://christopherzita.medium.com/how-to-download-google-images-using-python-2021-82e69c637d59
28)https://data.gov.in/
29)imagenet dataset-http://www.image-net.org/
30)https://parulpandey.com/2020/08/09/getting-datasets-for-data-analysis-tasks%e2%80%8a-%e2%80%8aadvanced-google-search/
31)https://storage.googleapis.com/openimages/web/index.html ,
https://storage.googleapis.com/openimages/web/visualizer/index.html?set=train&type=segmentation&r=false&c=%2Fm%2F09qck
https://console.cloud.google.com/marketplace/browse?filter=solution-type:dataset&_ga=2.35328417.1459465882.1589693499-869920574.1589693499
https://catalog.data.gov/dataset?groups=education2168#topic=education_navigation
https://vincentarelbundock.github.io/Rdatasets/datasets.html
32)coco dataset https://cocodataset.org/#explore
33)huggingface datasets-https://github.com/huggingface/datasets https://huggingface.co/datasets https://huggingface.co/languages
pip install datasets
34)Big Bad NLP Database-https://datasets.quantumstat.com/
fast.ai Datasets https://course.fast.ai/datasets
https://github.com/niderhoff/nlp-datasets
600 NLP Datasets and Glory https://pub.towardsai.net/600-nlp-datasets-and-glory-4b0080bf5ab
nlp-datasets https://github.com/karthikncode/nlp-datasets
https://analyticsindiamag.com/15-most-important-nlp-datasets/ https://medium.com/ai-in-plain-english/25-free-datasets-for-natural-language-processing-57e407402c60
35)https://www.edureka.co/blog/25-best-free-datasets-machine-learning/
36)bigquery public dataset ,Google Public Data Explorer
https://cloud.google.com/public-datasets https://guides.library.cmu.edu/machine-learning/datasets
37)inbuilt library data eg:iris dataset,mnist dataset,etc...
pandas-datareader https://github.com/pydata/pandas-datareader
tf.data.Datasets for TensorFlow Datasets
38)https://data.gov.sg/ https://data.gov.au/ https://data.europa.eu/euodp/en/data https://data.europa.eu/euodp/en/data https://data.govt.nz/
data.gov.be ,data.egov.bg/ ,data.gov.cz/english ,portal.opendata.dk,govdata.de,opendata.riik.ee,data.gov.ie,data.gov.gr,datos.gob.es,data.gouv.fr,data.gov.hr
dati.gov.it,data.gov.cy,opendata.gov.lt,data.gov.lv,data.public.lu,data.gov.mt,data.overheid.nl,data.gv.at,danepubliczne.gov.pl,dados.gov.pt,data.gov.ro,podatki.gov.si
data.gov.sk,avoindata.fi,oppnadata.se,https://data.adb.org/ ,https://data.iadb.org/ ,https://www.weforum.org/agenda/2018/03/latin-america-smart-cities-big-data/
https://data.fivethirtyeight.com/ , https://wiki.dbpedia.org/ ,https://www.europeandataportal.eu/en ,https://data.europa.eu/ ,https://www.census.gov/,
https://www.who.int/data/gho ,https://data.unicef.org/open-data/ ,http://data.un.org/ ,https://data.oecd.org/ ,https://data.worldbank.org/
39.Awesome Public Dataset- https://github.com/awesomedata/awesome-public-datasets
Get OpenML’s Dataset in One Line of Code https://mathdatasimplified.com/2021/04/23/fetch_openml-get-openmls-dataset-in-one-line-of-code/
https://github.com/the-pudding/data
datasets https://github.com/benedekrozemberczki/datasets
kdnuggets https://www.kdnuggets.com/datasets/index.html
Hub https://github.com/activeloopai/Hub
40.Datasets for Machine Learning on Graphs-https://ogb.stanford.edu/
41.https://www.johnsnowlabs.com/data/
42.30 largest tensorflow datasets-https://lionbridge.ai/datasets/tensorflow-datasets-machine-learning/
43. coco dataset-https://cocodataset.org/#home
flickr-downloader https://github.com/renatoviolin/flickr-downloader/
Google Open images-https://opensource.google/projects/open-images-dataset https://storage.googleapis.com/openimages/web/index.html
50+ Object Detection Datasets-https://medium.com/towards-artificial-intelligence/50-object-detection-datasets-from-different-industry-domains-1a53342ae13d
70+ Image Classification Datasets from different Industry domains-https://medium.com/towards-artificial-intelligence/70-image-classification-datasets-from-different-industry-domains-part-2-cd1af6e48eda
VisualData Discovery https://www.visualdata.io/discovery https://guides.library.cmu.edu/machine-learning/datasets
data https://storage.googleapis.com/openimages/web/visualizer/index.html?c=%2Fm%2F04yqq2&r=false&set=train&type=segmentation&utm_campaign=Weekly%20Machine%20Learning%20news&utm_medium=email&utm_source=Revue%20newsletter
VisualData https://www.visualdata.io/discovery
bifrost- https://datasets.bifrost.ai/
satellite images https://towardsdatascience.com/finding-satellite-images-for-your-data-science-project-888695361925
https://public.roboflow.com/
https://www.visualdata.io/discovery http://www.image-net.org/ https://www.cs.toronto.edu/~kriz/cifar.html
tensorflow_datasets.object_detection - https://storage.googleapis.com/openimages/web/index.html
https://github.com/google-research-datasets/Objectron/ https://ai.googleblog.com/2020/11/announcing-objectron-dataset.html?m=1
http://idd.insaan.iiit.ac.in/ http://database.mmsp-kn.de/koniq-10k-database.html
https://ai.googleblog.com/2020/11/announcing-objectron-dataset.html
https://www.visualdata.io/discovery https://blogs.bing.com/maps/2019-03/microsoft-releases-12-million-canadian-building-footprints-as-open-data
https://blogs.bing.com/maps/2019-09/microsoft-releases-18M-building-footprints-in-uganda-and-tanzania-to-enable-ai-assisted-mapping
https://datasets.bifrost.ai/ https://storage.googleapis.com/openimages/web/download.html https://computervisiononline.com/datasets http://yacvid.hayko.at/
https://www.cogitotech.com/use-cases/biodiversity/
ImageNet data -http://image-net.org/
ApolloScape Dataset-http://apolloscape.auto/
https://github.com/chrieke/awesome-satellite-imagery-datasets
44.https://github.com/fivethirtyeight/data
45.Recommender Systems Datasets-https://cseweb.ucsd.edu/~jmcauley/datasets.html
46.indiadataportal-https://indiadataportal.com/
47.US Government Open Dataset: https://www.data.gov/
https://censusreporter.org/ https://data.census.gov/cedsci/
48.AWS Public Data Sets:https://registry.opendata.aws/ https://aws.amazon.com/opendata/?wwps-cards.sort-by=item.additionalFields.sortDate&wwps-cards.sort-order=desc
49.https://the-eye.eu/public/AI/pile_preliminary_components/
Reddit -https://www.reddit.com/r/datasets/
wikipedia-https://en.wikipedia.org/wiki/List_of_datasets_for_machine-learning_research
http://opendata.cern.ch/ , https://www.imf.org/en/Data
Global Health Observatory data repository-https://apps.who.int/gho/data/node.main
CERN Open Data Portal-http://opendata.cern.ch/
TensorFlow Datasets https://www.tensorflow.org/datasets
50.openblender- https://www.openblender.io/#/welcome
51.Top 10 Datasets For Cybersecurity Projects- https://analyticsindiamag.com/top-10-datasets-for-cybersecurity-projects/
52.Datasets from Web Crawl Data (nlp)-http://data.statmt.org/cc-100/
53.https://www.springboard.com/blog/free-public-data-sets-data-science-project/
54.NASA - https://nasa.github.io/data-nasa-gov-frontpage/ace
55.Academic Torrents,GitHub Datasets,CERN Open Data Portal,Global Health Observatory Data Repository
56.32 Data Sets to Uplift your Skills in Data Science-https://blog.datasciencedojo.com/data-sets-data-science-skills/?utm_content=144243072&utm_medium=social&utm_source=linkedin&hss_channel=lcp-3740012
https://lionbridge.ai/datasets/the-50-best-free-datasets-for-machine-learning/
57.OpenDaL-https://opendatalibrary.com/
Data Is Plural-https://docs.google.com/spreadsheets/d/1wZhPLMCHKJvwOkP4juclhjFgqIY8fQFMemwKL2c64vk/edit#gid=0
VisualData-https://www.visualdata.io/discovery
https://medium.com/towards-artificial-intelligence/best-datasets-for-machine-learning-data-science-computer-vision-nlp-ai-c9541058cf4f
58.Pandas Data Reader-https://pandas-datareader.readthedocs.io/en/latest/remote_data.html
59.ieee-dataport-https://ieee-dataport.org/datasets
https://medium.com/towards-artificial-intelligence/best-datasets-for-machine-learning-data-science-computer-vision-nlp-ai-c9541058cf4f
https://github.com/neomatrix369/awesome-ai-ml-dl/blob/master/data/datasets.md#datasets-and-sources-of-raw-data
60.Generating Realistic Fake Data https://towardsdatascience.com/free-resources-for-generating-realistic-fake-data-da63836be1a8
Full Synthetic Data ,Partial Synthetic Data,Hybrid Synthetic Data
Faker is a Python package that generates fake data-https://github.com/joke2k/faker
ydata-synthetic,Gretel,gretel-synthetics,GenerateData,DataSynthesizer,SDV,SDGym,SDMetrics,Copulas,gretel-synthetics,kubric,CTGAN,Synthea,synthia,nbsynthetic ,pydbgen,synthpop,faker,Tonic,ydata,Mostly AI,Mirry.ai,Hazy,Gretel,Diveplane,Datagen,Mimesis,faker,FauxFactory,Radar,PikaAccelario,Chooch,Datagen,Datomize,Deep Vision Data,Monitaur,MOSTLY AI,OpenSynthetics,Replica Analytics,Scale AI,SKY ENGINE AI,Synthesis AI,Plaitpy,TimeseriesGenerat,Accelario,Chooch,dgutils,AI.Reverie,Kinetic Vision,SynthDet,OpenSynthetics,Mockaroo,GenerateData,JSON Schema Faker,FakeStoreAPI,Mock Turtle,nbsynthetic,AiFi,AI.Reverie,Anyverse,Cvedia,DataGen,Diveplane,Gretel,Hazy,Mostly AI,OneView,TRGD,YDATA Synthetic,SDV,Tonic.AI,Mostly.AI,Parallel Domain,Mindtech,Synthesis AI,Oneview,Hazy,CVEDIA,SKY ENGINE AI,Edgecase.ai,Statice,ANYVERSE,Rendered.ai,Datomize,Facteus,Gretel,Synthesized,Syntheticus,Syntho,Tonic, kubric,Stable Diffusion,Parallel Domain,Mindtech,Synthesis AI,Oneview,MOSTLY AI,Hazy,CVEDIA,SKY ENGINE AI,Edgecase.ai,Statice,ANYVERSE,Rendered.ai,Datomize,Facteus,Gretel,Synthesized,Syntheticus,Syntho,Tonic,MOSTLY AI, GenRocket, YData, Hazy, and MDClone ,Gretel, MOSTLY AI, Hazy, Statice ,NVIDIA Omniverse, OneView, CVEDIA, Datagen, Parallel Domain,Infinity AI,Parallel Domain,Rendered.AI,Scale.AI,SKY ENGINE AI,Synthesis AI,Paella,statice,DataSynthesizer,Pydbgen,TimeseriesGenerator,Mimesis,Synthesized,Syntheticus,Syntho,Tonic,Clearbox AI ,RDT (Reversible Data Transforms),DeepEcho
Models: GANs, CTGAN, WGAN, WGAN-GP, VAEs,GANs, TimeGAN, AR
GAN-based Deep Learning data synthesizer CTGAN,CopulaGAN,Synthetic Data Vault,Probabilistic AutoRegressive model
Extract the metadata using DataDescriber, Compare the input and synthetic data using ModelInspector
Mockaroo https://www.mockaroo.com/
GenerateData https://site.generatedata4.com/
JSON Schema Faker https://json-schema-faker.js.org/
FakeStoreAPI https://fakestoreapi.com/
graviti dataset https://gas.graviti.com/open-datasets
Synthetic data for computer vision https://github.com/ZumoLabs/zpy
GANs for Tabular Synthetic Data Generation https://github.com/Diyago/GAN-for-tabular-data
Synthetic Image Datasets https://analyticsindiamag.com/unity-launches-synthetic-image-datasets-to-train-ai-models-faster/
Synthetic structured data generators https://github.com/ydataai/ydata-synthetic
gretel Synthetic Data API https://gretel.ai/
Timeseries DGAN https://synthetics.docs.gretel.ai/en/latest/models/timeseries_dgan.html
DatasetGAN: an automatic procedure to generate massive datasets of high-quality images
Generating synthetic tabular data with GANs,Synthetic Time-Series Data by A GAN approach
Unity Launches Synthetic Image Datasets https://www.marktechpost.com/2021/04/23/unity-launches-synthetic-image-datasets-to-train-ai-and-computer-vision-models-faster/
Generate Your Own Dataset using GAN https://www.analyticsvidhya.com/blog/2021/04/generate-your-own-dataset-using-gan/
accurate of synthetic data https://gretel.ai/blog/how-accurate-is-my-synthetic-data
Synthetic data library https://github.com/finos/datahub https://github.com/agmmnn/awesome-blender https://opendata.blender.org/ https://www.youtube.com/watch?v=eZwOeBkLL8E
https://www.kdnuggets.com/2019/09/scikit-learn-synthetic-dataset.html
Fully Synthetic Data,Partially Synthetic Data ,Hybrid Synthetic Data https://towardsdatascience.com/synthetic-data-key-benefits-types-generation-methods-and-challenges-11b0ad304b55
Synthetic Image Datasets https://analyticsindiamag.com/unity-launches-synthetic-image-datasets-to-train-ai-models-faster/ https://dockship.io/articles/607847e461373d1b994cc2dc/create-synthetic-images-using-opencv-(python)
gretel-synthetics Synthetic data generators for structured and unstructured text, featuring differentially private learning. https://github.com/gretelai/gretel-synthetics
Synthetic Data Generation Using Gaussian Mixture Model https://deepnote.com/@chanakya-vivek-kapoor/Synthetic-Data-Generation-QaaTRs73T2iCb0amHFbwpQ
Synthetic Data Vault https://analyticsindiamag.com/guide-to-synthetic-data-vault-an-ecosystem-of-synthetic-data-generation-libraries/ https://github.com/sdv-dev/SDV
Create Your own Image Dataset using Opencv https://www.analyticsvidhya.com/blog/2021/05/create-your-own-image-dataset-using-opencv-in-machine-learning/
ydata-synthetic https://github.com/ydataai/ydata-synthetic
Table Evaluator About Evaluate real and synthetic datasets with each other https://github.com/Baukebrenninkmeijer/table-evaluator
evaluate quality and efficacy of synthetic datasets SDMetrics https://github.com/sdv-dev/SDMetrics
61.Text Data Annotator Tool - Datasaur https://datasaur.ai/
Tagalog is our state-of-the-art solution for data management and labeling in Natural Language Processing https://www.tagalog.ai/tagalog/
62.Google Analytics cost data import https://segmentstream.com/google-analytics?utm_source=twitter&utm_medium=cpc&utm_campaign=ga_costs_import_en&utm_content=guide
63.https://lionbridge.ai/services/crowdsourcing/ https://lionbridge.ai/ https://www.clickworker.com/ https://appen.com/ https://www.globalme.net/
64.Azure Open Datasets https://azure.microsoft.com/en-us/services/open-datasets/ https://azure.microsoft.com/en-in/services/open-datasets/catalog/
Yelp Open Dataset https://www.yelp.com/dataset
https://data.world/
ODK Open Data Kit- https://getodk.org/
World Bank Open Data https://data.worldbank.org/
https://analyticsindiamag.com/10-biggest-data-breaches-that-made-headlines-in-2020/
https://data.mendeley.com/
https://github.com/iamtekson/geospatial-data-download-sites
https://eugeneyan.com/writing/data-discovery-platforms/
65.https://medium.com/towards-artificial-intelligence/best-datasets-for-machine-learning-data-science-computer-vision-nlp-ai-c9541058cf4f
https://towardsdatascience.com/data-repositories-for-almost-every-type-of-data-science-project-7aa2f98128b
https://github.com/MTG/freesound-datasets
https://dataform.co/
https://github.com/rfordatascience/tidytuesday https://www.youtube.com/watch?v=vCBeGLpvoYM
https://www.analyticsvidhya.com/blog/2020/12/top-15-datasets-of-2020-that-every-data-scientist-should-add-to-their-portfolio/?utm_source=linkedin&utm_medium=AV|link|high-performance-blog|blogs|44181|0.375
https://cseweb.ucsd.edu/~jmcauley/datasets.html
66.https://en.wikipedia.org/wiki/List_of_datasets_for_machine-learning_research
https://archive.org/details/datasets
https://commoncrawl.org/
https://www.youtube.com/watch?v=1aUt8zAG09E
67. 6 Sources of Financial Data https://medium.datadriveninvestor.com/financial-data-431b75975bb
yfinance for finance data using https://github.com/ranaroussi/yfinance https://medium.com/towards-artificial-intelligence/algorithmic-trading-with-python-and-machine-learning-part-1-47c56706c182
import fix_yahoo_finance as yf , yahoofinancials ,Pandas DataReaders,Twelve Data
financeapi https://towardsdatascience.com/pull-and-analyze-financial-data-using-a-simple-python-package-83e47759c4a7
Investing.com pip install investpy ,Kite by Zerodha pip install kiteconnect,quandl pip install quandl
https://www.analyticsvidhya.com/blog/2021/01/bear-run-or-bull-run-can-reinforcement-learning-help-in-automated-trading/?utm_source=feedburner&utm_medium=email&utm_campaign=Feed%3A+AnalyticsVidhya+%28Analytics+Vidhya%29
Downloading Historical Stock prices with Alpha Vantage https://medium.com/towards-artificial-intelligence/downloading-historical-stock-prices-with-alpha-vantage-688edad46a6d
Pandas Datareader https://pandas-datareader.readthedocs.io/en/latest/ https://www.youtube.com/watch?v=f2BCmQBCwDs
Get Financial Data Directly into Python https://www.quandl.com/tools/python https://medium.com/nerd-for-tech/how-to-get-financial-data-using-python-7a508f25fc39
openml https://www.openml.org/search?type=data
https://registry.opendata.aws/
voice_datasets https://github.com/jim-schwoebel/voice_datasets
Dynamically-Generated-Hate-Speech-Dataset https://github.com/bvidgen/Dynamically-Generated-Hate-Speech-Dataset
68.DOCANO, an open source text annotation tool https://github.com/doccano/doccano
69.https://www.dataquest.io/blog/free-datasets-for-projects/
70.audio set https://research.google.com/audioset/
71.FlatData Flat explores how to make it easy to work with data in git and GitHub https://octo.github.com/projects/flat-data?utm_campaign=Data_Elixir&utm_source=Data_Elixir_337
72.Snorkel is an open-source Python library for programmatically building training datasets without manual labeling. https://www.snorkel.org/ https://towardsdatascience.com/snorkel-programmatically-build-training-data-in-python-712fc39649fe
2.Feature engineering
Feature-engine https://trainindata.medium.com/feature-engine-a-new-open-source-python-package-for-feature-engineering-29a0ab88ea7c https://feature-engine.readthedocs.io/en/latest/ https://github.com/solegalli/feature_engine https://www.datasciencecentral.com/profiles/blogs/feature-engine-python-package-for-feature-engineering
Automated feature engineering https://medium.com/ibm-data-ai/automated-feature-engineering-for-relational-data-with-autoai-3612fafe9f89
Automated Data Wrangling https://catalyst.coop/2021/05/23/automated-data-wrangling/
Automatic Feature Engineering Using Featurewiz https://towardsdatascience.com/automate-your-feature-selection-workflow-in-one-line-of-python-code-3d4f23b7e2c4 https://github.com/AutoViML/featurewiz
Automatic Feature Engineering Using AutoFeat https://analyticsindiamag.com/guide-to-automatic-feature-engineering-using-autofeat/
Upgini accuracy improving features https://github.com/upgini/upgini https://upgini.com/
Categorical Encoding https://github.com/scikit-learn-contrib/category_encoders
lazytransform https://github.com/AutoViML/lazytransform
Streamlining Feature Engineering Pipelines with Feature-engine https://towardsdatascience.com/streamlining-feature-engineering-pipelines-with-feature-engine-e781d551f470 https://feature-engine.readthedocs.io/en/latest/#
Validate your Data (Schema) https://towardsdatascience.com/introduction-to-schema-a-python-libary-to-validate-your-data-c6d99e06d56a
Validate Your pandas DataFrame with Pandera https://github.com/pandera-dev/pandera
Statistical DataFrame Testing Toolkit https://pandera.readthedocs.io/en/stable/index.html
Data storing format:Pickle,Parquet,Feather,Avro,ORC
Data cleaning-Pyjanitor-https://analyticsindiamag.com/beginners-guide-to-pyjanitor-a-python-tool-for-data-cleaning/
data cleaning library https://www.analyticsvidhya.com/blog/2021/05/data-cleaning-libraries-in-python-a-gentle-introduction/
Mage https://github.com/mage-ai/mage-ai
Cleaner Data Analysis with Pandas Using Pipes https://towardsdatascience.com/cleaner-data-analysis-with-pandas-using-pipes-4d73770fbf3c
DataPrep https://dataprep.ai/ https://github.com/sfu-db/dataprep https://towardsdatascience.com/dataprep-v0-3-0-has-been-released-be49b1be0e72
Dora (pip library) - data cleaning
Dora,PrettyPandas,DataCleaner,Tabulate,Pyjanitor,OpenRefine,cleanlab,pandera
https://github.com/sfu-db/dataprep https://github.com/akanz1/klib https://www.bitrook.com/ https://github.com/rhiever/datacleaner https://github.com/johnkerl/miller
cleanlab data-centric AI and machine learning with label errors, finding mislabeled data, and uncertainty quantification. Works with most datasets and models https://github.com/cleanlab/cleanlab
cleantext https://www.youtube.com/watch?v=i2TjAgga1YU&feature=youtu.be
CleanText: A Python Package to Clean Raw Text Data https://analyticsindiamag.com/guide-to-cleantext-a-python-package-to-clean-raw-text-data/
ATOM https://github.com/tvdboom/ATOM https://towardsdatascience.com/how-to-test-multiple-machine-learning-pipelines-with-just-a-few-lines-of-python-1a16cb4686d
openrefine A free, open source, powerful tool for working with messy data https://openrefine.org/#
data leaning library https://www.analyticsvidhya.com/blog/2021/05/data-cleaning-libraries-in-python-a-gentle-introduction/
https://machinelearningmastery.com/basic-data-cleaning-for-machine-learning/
Speed Up Data Cleaning and Exploratory Data Analysis in Python with klib https://github.com/akanz1/klib https://towardsdatascience.com/speed-up-your-data-cleaning-and-preprocessing-with-klib-97191d320f80
missingno https://github.com/ResidentMario/missingno
Take the Pain Out of Data Cleaning for Machine Learning https://towardsdatascience.com/take-the-pain-out-of-data-cleaning-for-machine-learning-20a646a277fd
dabl https://ms-bharti.medium.com/jump-start-your-supervised-learning-task-with-dabl-e479323e81fe
Easy to use Python library of customized functions for cleaning and analyzing data https://github.com/akanz1/klib
PyOD https://pyod.readthedocs.io/en/latest/ https://github.com/yzhao062/pyod/blob/development/docs/index.rst https://towardsdatascience.com/how-to-detect-outliers-with-python-pyod-aa7147359e4b
Amazon’s New Visual Data Cleaning Tool Can Speed Up Your AI Projects https://medium.com/dataseries/how-amazons-new-visual-data-tool-can-speed-up-your-ai-projects-68e3289382c
Featuretools https://www.featuretools.com/ https://towardsdatascience.com/why-automated-feature-engineering-will-change-the-way-you-do-machine-learning-5c15bf188b96
https://github.com/alteryx/featuretools https://analyticsindiamag.com/introduction-to-featuretools-a-python-framework-for-automated-feature-engineering/
Feature Selection using Genetic Algorithm https://github.com/kaushalshetty/FeatureSelectionGA
AutoFeat https://analyticsindiamag.com/guide-to-automatic-feature-engineering-using-autofeat/ https://github.com/cod3licious/autofeat
feast Feature Store for Machine Learning https://github.com/feast-dev/feast https://www.youtube.com/watch?v=ZeJdr0nZ9PA
Category Encoders https://contrib.scikit-learn.org/category_encoders/
Feature-engine https://feature-engine.readthedocs.io/en/latest/index.html
FeatureTools,AutoFeat,TsFresh,Cognito,OneBM,ExploreKit,PyFeat,Category Encoders,Feature-engine
Automated Feature Selection: Featurewiz https://github.com/AutoViML/featurewiz https://towardsdatascience.com/featurewiz-fast-way-to-select-the-best-features-in-a-data-9c861178602e
zoofs a Python library for performing feature selection https://github.com/jaswinder9051998/zoofs
Feature Engineering of DateTime Variables for Data Science, Machine Learning https://www.kdnuggets.com/2021/04/feature-engineering-datetime-variables-data-science-machine-learning.html
NeatText a simple NLP package for cleaning textual data and text preprocessing https://github.com/Jcharis/neattext
Remove duplicate data in dataset,Data validity check,Contaminated Data,Inconsistent Data,Invalid Data,
Feature Selection
1.Removal of arbitraty features: DropFeatures
Removing unused columns,Removing Constant features,Removing Constant Features using VarianceThreshold,Removing Quasi-Constant Features,Removing Duplicate Columns
2.Removal of constant and almost constant features: DropConstantFeatures
Removal of Low Variance
removal of irrelevant data
3.Removal of duplicated variables: DropDuplicateFeatures
4.Removal of correlated features: DropCorrelatedFeatures, SmartCorrelatedSelection
Drop features that have a poor correlation with the response variable
5.Selection of features by value shuffling: SelectByShuffling
Selection of features by High correlation with the target variable
6.Selection of features by univariate performance: SelectBySingleFeaturePerformance
7.Selection of features by target encoding: SelectByTargetMeanPerformance
8.Recursive Feature Elimination: RecursiveFeatureElimination
9.Recursive Feature Addition: RecursiveFeatureAddition
stats,Scipy,Pingouin,Statsmodels,SymPy,Sage,
StatisticsGen component computes statistics
Check data types , Handle duplicate values
a.Handle missing value
Types of missing value https://datamuni.com/@atsunorifujita/missing-value-imputation-using-datawig
Handling Missing Values in Pandas https://pub.towardsai.net/handling-missing-values-in-pandas-f87cec928937
Identify the source of missing data
i.missing completely at random(no correlation b/w missing and observed data) we can delete no disturbance of data distribution
ii.missing at random (randomness in missing data, missing value have correlation by data) we can't delete because disturbance of data distribution
iii.missing not at random (there is reason for missing value and directly related to value)
iv.structured missing 100 % sure on why it is missing
Identify Missingness Types With Missingno https://towardsdev.com/how-to-identify-missingness-types-with-missingno-61cfe0449ad9
Univariate,Multivariate https://medium.com/fintechexplained/what-are-imputers-in-data-science-b72f8308322b
univariate imputation impute on 1 column multi variate imputation impute on 1 or more column
1.if missing data too small then delete it a.row deletion b.column deletion c.pairwise deletion and listwise deletion
Drop based on a threshold value,Drop using a subset of columns
2.replace by statistical method mean(influenced by outiler),median(not influenced by outiler),mode , minimum, maximum,Zero,Constant
Fill with Mean / Median of Column or Group Forward Fill or Forward Fill within Groups
Mean and Median Fill with Groupby
Pass another DataFrame to fillna function to fill up the missing values.
Similar case Imputation
3.apply classifier algorithm to predict missing value
Using Algorithms that support missing values
Imputation using Deep Learning Library — Datawig https://github.com/awslabs/datawig
4.Simple Imputer,and Multiple Imputation ,Iterative imputer,knn imputer, multivariate imputation, Verstack — NaNImputer,Impyute —MICE ,Substitution
5.apply unsupervised
6.Random Imputation,Iterative Imputation,Random Sample imputation
7.Adding a variable to capture NAN(missing term),Imputation with the string ‘Missing’,Adding missing idicator
8.Arbitrary Value Imputation
TREAT MISSING VALUES AS A SEPARATE CATEGORY
ue DOMAIN KNOWLEDGE
9.hot deck Imputation,Cold deck imputation
10.regression Imputation,Stochastic Regression Imputation,Interpolation and Extrapolation
11.End of Distribution Imputation
12.Arbitrary Value Imputation
13.Frequent Category Imputation
14.MICE Imputation,miceforest ( https://github.com/AnotherSamWilson/miceforest )
Miss Forest https://github.com/stekhoven/missForest
15.interpolation https://www.analyticsvidhya.com/blog/2021/06/power-of-interpolation-in-python-to-fill-missing-values/ Interpolate or Interpolate within Groups
LINEARINTERPOLATION ,POLYNOMIALINTERPOLATION,INTERPOLATION THROUGH PADDING
Extrapolation and Interpolation ,Time-Based Interpolation,Spline Interpolation,Linear Interpolation,Smoothing, interpolation,Bidirectional Recurrent Imputation for Time Series (
16.Last Observation Carried Forward (LOCF) , Next Observation Carried Backward , Rolling Statistics, Interpolation
Single and Multiple Imputation,Univariate Imputation,Multivariate Imputation ,Iterative Imputer,MissForest Imputation,Stochastic Regression Imputation, Multiple Imputations, Datawig, Hot-Deck imputation, Extrapolation, Interpolation
datawig Imputation of missing values in tables https://github.com/awslabs/datawig
Imputation using K-NN,missForest,Random Forest-based Imputation,missingpy,som,Ann,mlp
Model based procedure gaussian mixture model
Imputation Using Deep Learning (Datawig),neural network for imputation,BRITS
15.autoimpute-https://github.com/kearnz/autoimpute
16.bfill / ffill Back Fill or Back Fill within Groups
17.Adding a variable to capture NAN
18.replace NAN with a new category
19.Missing indicator
After drop or imputation feature distribution should be same
https://www.kdnuggets.com/2021/05/deal-with-categorical-data-machine-learning.html
https://towardsdatascience.com/6-different-ways-to-compensate-for-missing-values-data-imputation-with-examples-6022d9ca0779
https://stefvanbuuren.name/fimd/want-the-hardcopy.html https://www.datasciencecentral.com/profiles/blogs/how-to-treat-missing-values-in-your-data-1
20.Imputation with the string ‘Missing’ ,Addition of binary missing indicators
21.Algorithms robust to missing values - LightGBM
datawig imputation https://github.com/awslabs/datawig
22.Cluster-based approach for missing value imputation Naive clustering,Column-sensitive clustering
Top Data Cleaning Tools https://www.marktechpost.com/2022/02/20/top-data-cleaning-tools-for-data-science-and-machine-learning-projects-in-2022/
OpenRefine https://openrefine.org/ https://github.com/OpenRefine/OpenRefine
Data Ladder https://dataladder.com/
re-data fix data issues https://github.com/re-data/re-data
Automatically find and fix errors in your ML datasets. https://github.com/cleanlab/cleanlab
Clean APIs for data cleaning https://github.com/pyjanitor-devs/pyjanitor
datacleaner https://github.com/rhiever/datacleaner
https://github.com/akanz1/klib https://pyjanitor-devs.github.io/pyjanitor/ https://dataprep.ai/ https://scrubadub.readthedocs.io/en/latest/index.html https://www.bitrook.com/
AutoClean https://github.com/elisemercury/AutoClean
Dora,PrettyPandas,DataCleaner,Tabulate,Pyjanitor
b.Handle imbalance Collect More Data if possible,Try Resampling Your Dataset
1.Under Sampling - mostly not prefer because lost of data imbalaced-learn,tomek links,Random Under-Sampling, Edited Nearest Neighbours,NearMiss
Random majority under-sampling with replacement,Tomek Links Undersampling,Under-sampling with Cluster Centroids,Condensed Nearest Neighbour,One-Sided Selection,Neighboorhood Cleaning Rule,One-Sided Selection,
2.Over Sampling (RandomOverSampler (here new points create by same dot)) , SMOTETomek(new points create by nearest point so take long time),BorderLine Smote,Borderline-SMOTE SVM,FAIR SMOTE,DBSMOTE,SMOTE-ENN ,KMeans Smote,SVM Smote,SMOTe NC,ENNSMOTE,SVMSMOTE,MOTE-N ADASYN,ADASYN,Smote-NC,Random Over Sampling,RandomUnderSampler,SMOTEN,SMOTE-Tomek,SMOTE-ENN,SMOTE-CUT,Cluster-Based Over Sampling, Informed Over Sampling,MSMOTE,Oversampling Using Gaussian Mixture Models,SMOTE + Tomek Links, SMOTE + ENN,Crucio SMOTEENN,NearMiss,OSS & NCR — under sampling,Borderline SMOTE KNN,Borderline SMOTE SVM,Adaptive Synthetic Sampling (ADASYN),BalancedBaggingClassifier() , BalancedRandomForestClassifier SMOTE-NC
Over-sampling followed by under-sampling : SMOTE + Tomek links,SMOTE + ENN
smote_variants https://github.com/analyticalmindsltd/smote_variants
https://towardsdatascience.com/5-smote-techniques-for-oversampling-your-imbalance-data-b8155bdbe2b5
https://www.analyticsvidhya.com/blog/2017/03/imbalanced-data-classification/
ensmble based -Bagging Based techniques, Boosting-Based techniques,Adaptive Boosting- Ada Boost techniques,Gradient Tree Boosting,XG Boost
tools Imb-learn,SMOTE-Variants,Regression-ReSampling https://towardsdatascience.com/tools-to-handle-class-imbalance-bff20c3bf099
Balancing data sets with Crucio ADASYN https://medium.com/softplus-publication/balancing-data-sets-with-crucio-adasyn-79f04ff0779d
LoRAS: A Better Oversampling Algorithm https://analyticsindiamag.com/hands-on-guide-to-loras-a-better-oversampling-algorithm/ https://github.com/narek-davtyan/LoRAS
https://towardsdatascience.com/7-over-sampling-techniques-to-handle-imbalanced-data-ec51c8db349f
Combining Over and Under-sampling
3.class_weight give more importance(weight) to that small class ( Cost-Sensitive Algorithms)
from sklearn import compute_class_weight
Cost-sensitive learning,Class-balanced loss,Focal loss
weighted loss function
4.use Stratified kfold to keep the ratio of classess constantly, train teat spilt startify attribute
Use K-fold Cross-Validation in the Right Way,Stratified Cross Validation,repeated K-fold Cross-Validation,Stratified K-fold Cross-Validation
Stratified Sampling,Stratified splits
5.Weighted Neural Network
cluster based sampling
6.MESA https://analyticsindiamag.com/guide-to-mesa-boost-ensemble-imbalanced-learning-with-meta-sampler/
7.choose Proper Evaluation Metric metric roc,f1,etc...
https://machinelearningmastery.com/framework-for-imbalanced-classification-projects/ https://www.kdnuggets.com/2020/01/5-most-useful-techniques-handle-imbalanced-datasets.html
8.Deep Imbalanced Regression https://github.com/YyzHarry/imbalanced-regression https://analyticsindiamag.com/deep-imbalanced-regression-complete-guide/
Imbalanced Dataset Sampler https://github.com/ufoym/imbalanced-dataset-sampler
9.Ensemble Techniques ensemble techinque - Bagging Based techniques,Boosting-Based techniques
BalancedBaggingClassifier,Threshold moving,Easy Ensemble classifier,Balanced Random Forest,Balanced Bagging,RUSBoost,MESA
10.Try Different Algorithms (ensemble techinque - Bagging Based techniques,Boosting-Based techniques)
model based (some models are particularly suited for imbalanced dataset)
Algorithmic Ensemble Techniques,Tree-Based Algorithms
11.Try a Different Perspective ( consider as anomaly detection or change detection)
Threshold Moving Methods,One-Class Classification,Customised Ensemble Algorithms
Probability Tuning Algorithms,Calibrating Probabilities,Tuning the Classification Threshold
12.databalancer https://github.com/pradeepdev-1995/databalancer
13.collect more data
14.treat problem as anomaly detection
15.Combined Class Methods
In this type of method, various methods are fused together to get a better result to handle imbalance data. For instance, like SMOTE can be fused with other methods like MSMOTE (Modified SMOTE), SMOTEENN (SMOTE with Edited Nearest Neighbours), SMOTE-TL, SMOTE-EL, etc. to eliminate noise in the imbalanced data sets
16.One-Class Algorithms,One-Class Support Vector Machines,Isolation Forests,Minimum Covariance Determinant,Local Outlier Factor,Mahalanobis Distance for One Class Classification
17.BalancedBatchGenerator https://imbalanced-learn.org/stable/references/generated/imblearn.keras.BalancedBatchGenerator.html
18.train_test_split stratify attribute , stratify split
19. https://github.com/pradeepdev-1995/databalancer
Meta’s balance package https://github.com/facebookresearch/balance
c.Remove noise data
d.Format data
d.Discretize a.Equal width binning b.Equal frequency binning c.K-means Binning d.Discretization by Decision Trees e.ChiMerge f.Arbitrary Discretization g.Quantile h.Custom Discretization
Discretisation plus categorical encoding,Discretisation plus encoding Discretisation with classification trees,Domain knowledge discretisation
Data Binning
Binning based on distribution (quantile-cut),Binning based on values (cut)
Bucketing , quantile bucketing ,Clipping
e.Handle categorical data Ordinal,Nominal,cyclic,binary categorical variables
1.One Hot Encoding , dummy, and effect coding,Similarity Encoding,Binary Encoding
Rainbow Method for Label Encoding
2.Count Or Frequency Encoding
3.Ordinal encoding,Nominal Encoding,Monotonic ordinal encoding,Target Guided Ordinal Encoding,Target Guided Mean Encoding,Target-Mean-Encoding
4.Target encoding / Mean encoding,GapEncoder,MinHashEncoder,Target guided ordinal encoding,Bayesian Target Encoding
Target Encoding,K-Fold Target Encoding,Leave-One-Out Target Encoding,Leave One fold out Target Encoding,Target Encoding with a Weighted Mean
5.Probability Ratio Encoding,Rank Encoding,Polynomial Encoding,Backward Difference Encoding
6.label encoding or .cat.codes ,Label Encoding with Rainbow Method
7.probability ratio encoding
8.woe(Weight_of_evidence)
Word2Vec(word Word embedding)
9.one hot encoding with multi category (keep most frequently repeated only) (One hot encoding of top categories)
10.feature hashing,CatBoost Encoding
11.sparse csr matrix
12.entity embeddings,Categorical Embeddings
13.binary encoding,Base-N Encoding
14.Rare label encoding
15.Leave-one-out(Loo) encoding,Generalized Linearn Mixed Model
16.hash encoding,MinHashEncoder,SimilarityEncoder,DatetimeEncoder,SuperVectorizer,FeatureHasher,DictVectorizer,HashingVectorizer,DecisionTreeEncoder
17.dummy encoding,NaN Encoding,bin counting scheme,effect coding scheme
18.Helmert Encoding,Backward Difference Encoding,James-Stein Encoding,M-estimator Encoding,Thermometer Encoder,Bayesian Encoders,Effect Encoding
Helmert Encoding,Base N Encoding,Hash Encoding,Effect or Sum or Deviation Encoding,Backward Difference Encoding,M-Estimator Encoding,James- Stein Encoding,Thermometer Encoding,CatBoost Encoding,Backward Difference Encoding,Binary Encoding,NaN encoding Polynomial encoding,Expansion encoding,Probability Ratio,Binary encoding,cat boost encoder,glm encoder,m-estimte,sum coding, polynomial Encoding,PRatioEncoder,DecisionTreeEncoder,Similarity Encoding,BackwardDifferenceEncoder GapEncoder,MinHashEncoder,TargetEncoder,Polynomial Encoding,James-Stein Encoding,MultiLabelBinarizer,SumEncoder,Quantile Encoder,Summary Encoder ,Base N Coding,Leaf Encoding,GLMM Encoding,James-Stein Encoding,Thermometer Encoding,Quantile Encoding,Summary Encoding,Collapsing Categories
Transform your categorical columns with imperio SmoothingTransformer
entity encoder for categorical variable https://contrib.scikit-learn.org/category_encoders/
Automatically selects the best encoder https://github.com/dirty-cat/dirty_cat
Improve ML Model Performance by Combining Categorical Features https://towardsdatascience.com/improve-ml-model-performance-by-combining-categorical-features-a23efbb6a215
https://towardsdatascience.com/beyond-one-hot-17-ways-of-transforming-categorical-features-into-numeric-features-57f54f199ea4
https://towardsdatascience.com/how-to-encode-categorical-data-d44dde313131 https://towardsdatascience.com/python-for-finance-7-useful-libraries-that-you-should-know-e422b9e9aaba
f.Scaling of data
1.Normalisation
2.Standardization(StandardScaler)
3.Robust Scaler not influenced by outliers because using of median,IQR
4.Min Max Scaling
5.Mean normalization
6.maximum absolute scaling
7.Power Transformer Scaler
8.Scaling To Median And Quantiles,Scaling to minimum and maximum values,Scaling to the vector norm
9.unit vector scaler
10.Z-score standardization
https://www.analyticsvidhya.com/blog/2020/07/types-of-feature-transformation-and-scaling/?utm_source=linkedin&utm_medium=KJ|link|high-performance-blog|blogs|44204|0.375
Probability and Statistics Packages : PyMC3, tensorflow-probability,Pyro,GPyTorch,hmmlearn,pomegranate,GPflow,patsy,pingouin,Orbit
Q-Q plot or Shapiro-Wilk Normality Test or lilliefors test or Jarque-Bera test or Kolmogorov-Smirnov or Anderson-Darling test is used to check whether feature is guassian or normal distributed required for linear regression,logistic regression to Improve
performance if not distributed then use below methods to bring it guassian distribution
normal test,Histogram,Q-Q plot,KDE plot,Skewness and Kurtosis for check normal distribution
Fitter Library Finding the Best Distribution that Fits Your Data https://towardsdatascience.com/finding-the-best-distribution-that-fits-your-data-using-pythons-fitter-library-319a5a0972e9
anderson teset use for check any distribution
Basic Distributions - PDF, PMF, CDF, PPF,Unform, Gaussian, Bernoulli, Multinomial,Normal Distribution,Poisson, Exponential, Geometric, Log-normal distribution, Pareto/Power Law Distribution
b.Logarithmic Transformation,LogCpTransformer
c.Reciprocal Trnasformation
d.Square Root Transformation
e.Exponential Transdormation
f.BoxCOx and Yeo-Johnson Transformation
g.log(1+x) Transformation
h.johnson
i.power transformations https://towardsdatascience.com/when-and-how-to-use-power-transform-in-machine-learning-2c6ad75fb72e
g.Quantile Transformation ,Arcsin Transformation , Inverse of Log,Inverse of Exponential,Inverse of Square Root,Square of Log,Square root of Exponential
Root transformation,Cube root transformation,Cosine Transformation,SplineTransformer,FunctionTransformer,ArcsinTransformer
Left skewness (use powers) Squares transformation,Cubes transformation,High powers
g.Remove low variance feature by using VarianceThreshold
remove Duplicate data,Low variation data,Irrelevant data,Incorrect data
remove Low entropy of categorical attributes
h.Same variable(only 1 variable) in feature then remove feature
i.Outilers removing outilers depond on problem we are solving https://github.com/jainyk/package-outlier
2 type of outilers available: Global outiler(single value/data point that deviates from the distribution), Local outiler,Contextual (conditional) outliers,Collective outliers(Group of datapoint deviates from the distribution)
eg: incase of fraud detection outilers are very important
methods to find outiler: Tukey’s fences ,KNN distance,Autoencoders,Standard Deviation,zscore,boxplot,scatter plot,histogram,Violin Plot,IQR,TensorFlow_Data_Validation,svm,One-Class SVM,Isolation Forest,kmeans,DBSCAN,K Means Clustering,Percentile,knn,autoencoder,local outiler factor,One-Class Classification,Medıan Absolute Devıatıon
Automatic Outlier Detection:Isolation Forest,DBSCAN,Local Outlier Factor,Standard Deviation Approach,K Means Clustering,Minimum Covariance Determinant,Robust Random Cut Forest,DBScan Clustering,One-Class Classification,One-Class SVM,Autoencoder,Outlier Detection using In-degree Number,Histogram-based Outlier Detection,Robust Covariance,PyNomaly,angle-based outlier detection (ABOD),k-Nearest Neighbors Detector,Elliptic Envelope,Cluster-based,Local Outlier Factor,Histogram-based Outlier Detection
outiler treatment: Keep them,mean/median/random imputation,drop,discretization (binning),Winsorization,treat as seperate group,replace with resperctive percentiles,standardize and scale the data,transformation(log,scaling,sqrt,power),Replace the outlier values with a suitable value (Like 3rd deviation),Percentile Based Flooring and Capping,Binning,Trimming,Treating outliers as missing values,Top/bottom/zero coding,winsorizing,robust scaler,log transformation,binning,regularisation,Discretization,arbitrary value
Outlier capping with IQR Outlier capping with mean and std Outlier capping with quantiles Arbitrary capping
Separation: If the amount of the outlier is higher than the normal then we can separate them from the main data and fit the model on them separately
Use a Different algorithm that is not sensitive to outliers
Segment data so outliers are in a separate group
Weighted means (which put more weight on the “normal” part of the distribution)
Trimming: Remove outliers from dataset. However, it can remove large proportion of data.
Capping: No data is removed. However, it distorts variable distribution.
Missing data: The outliers are treated as missing data.
Discretization: The outliers are put into lower and upper bins.
Arbitrary capping: Domain knowledge of the variable is required to cap the min and max
Winsorization: Truncate or cap extreme values to reduce the impact of outliers
Transformation: Apply logarithmic or square root transformations
Modeling techniques: Use robust regression or tree-based models
Outlier removal: Remove the values with careful consideration if they pose an extreme challenge
Separate Analysis : This involves performing separate analyses for the data with and without outliers
Flagging : Create an additional variable to indicate outliers, providing transparency about their presence in the dataset.
ML model which are not sensitive to outliers Like:-KNN,Decision Tree,SVM,NaïveBayes,Ensemble
PyOD: A Python Toolkit For Outlier Detection https://analyticsindiamag.com/guide-to-pyod-a-python-toolkit-for-outlier-detection/
TODS: An Automated Time-series Outlier Detection System https://github.com/datamllab/tods https://towardsdatascience.com/tods-detecting-outliers-from-time-series-data-2d4bd2e91381
anomalib anomaly detection library https://github.com/openvinotoolkit/anomalib
if outiler present then use robust scaling
alibi-detect https://github.com/SeldonIO/alibi-detect#adversarial-detection https://docs.seldon.io/projects/alibi-detect/en/latest/
https://medium.com/towards-artificial-intelligence/outlier-detection-and-treatment-a-beginners-guide-c44af0699754
https://towardsdatascience.com/two-outlier-detection-techniques-you-should-know-in-2021-1454bef89331
j.Anomaly anomaly-detection-resources https://github.com/yzhao062/anomaly-detection-resources
Types of Anomalies : Point anomalies,Contextual anomalies,Collective anomalies,Group Anomalies,Spatial Anomalies,Temporal Anomalies
clustering techniques to find it
Timetk https://towardsdatascience.com/timetk-the-r-library-for-time-series-analysis-9822f7720318
Isolation Forest(for Big Data),Z score,dbscan,Local Outlier Factor,One-Class Support Vector Machine,Autoencoders,knn,Time Series Analysis,Elliptic EnvelopeInterquartile Range,Median Absolute Deviation,K-Nearest Neighbours,Fast-MCD,Auto Encoders,K-means,Histogram-based,pca,K-means,Gaussian Mixture Model,Autoencoder,Hidden Markov Models (HMM)
𝐏𝐲𝐎𝐃
Local Correlation Integral (LCI),Histogram-based Outlier Detection (HBOS),Angle-based Outlier Detection (ABOD),Clustering-Based Local Outlier Factor (CBLOF),Minimum Covariance Determinant (MCD),Stochastic Outlier Selection (SOS),Spectral Clustering for Anomaly Detection (SpectralResidual),Feature Bagging,Average KNN,Connectivity-based Outlier Factor (COF),Variational Autoencoder (VAE)
bootstrapping to remove the influence of the outlier data
Anomaly detection using PyOD https://pyod.readthedocs.io/en/latest/ https://www.youtube.com/watch?v=QPjG_313GOw https://github.com/yzhao062/pyod https://pyod.readthedocs.io/en/latest/pyod.models.html
ADBench https://github.com/Minqi824/ADBench
Anomaly Detection Pyfbad https://github.com/Teknasyon-Teknoloji/pyfbad
divided into three types:Point/Global Anomalies,Collective Anomalies,Contextual Anomalies https://towardsdatascience.com/a-comprehensive-beginners-guide-to-the-diverse-field-of-anomaly-detection-8c818d153995
https://github.com/zhuyiche/awesome-anomaly-detection
https://medium.com/@ODSC/data-sciences-role-in-anomaly-detection-bd976f93d7e3
k.Sampling techniques
Random Sampling,Systematic Sampling,Cluster Sampling,Weighted Sampling,Stratified Sampling
a.biased sampling
b.unbiased sampling
l.Feature Creation
a.Combination of multiple features with mathematical operations
b.Combination of multiple features with a reference value
3.Exploratory Data Analysis(eda)
Explore the dataset by using python or microsoft Excel,Atoti,Power BI,Datapane’s,Tableau,TabPy,SAS Business Intelligence and Analytics Tool,QlikView,PyToQlik ,KNIME,Splunk,RapidMiner,Zoho Analytics,Sisense etc...
TabPy: Combining Python and Tableau https://www.kdnuggets.com/2020/11/tabpy-combining-python-tableau.html
atoti https://www.atoti.io/ https://www.youtube.com/watch?v=Hb6mSXa14oo Datapane’s Create a Beautiful Dashboard in Python in a Few Lines of Code https://towardsdatascience.com/datapanes-new-features-create-a-beautiful-dashboard-in-python-in-a-few-lines-of-code-a3c44523292b
Switching from Spreadsheets to Neptune.ai https://neptune.ai/blog/switching-from-spreadsheets-to-neptune-ai
Data Analysis using excel https://www.excel-easy.com/data-analysis.html https://www.educba.com/data-analysis-tool-in-excel/ https://www.youtube.com/watch?v=OOWAk2aLEfk
Power BI In Jupyter Notebooks https://github.com/microsoft/powerbi-jupyter https://analyticsindiamag.com/microsoft-releases-power-bi-in-jupyter-notebooks/
Mito Generating Python By Editing Spreadsheet https://www.youtube.com/watch?v=yy3-C39ra6s https://trymito.io/?source=twitter1
Automate Pivot Table with Python https://towardsdatascience.com/automate-excel-with-python-pivot-table-899eab993966
OpenPyXL: A Python Module For Excel https://analyticsindiamag.com/guide-to-openpyxl-a-python-module-for-excel/
causal interactive dashboards and beautiful visuals https://www.causal.app/,
Visual Programming (Orange) https://orange.biolab.si/
Integrating Tableau With Python https://analyticsindiamag.com/tabpy/ Qlib https://analyticsindiamag.com/qlib/
Data visualization (Matplotlib,Seaborn,DASH,Plotly,Plotly-Express,pyqtgraph,Bokeh,Pandas-Bokeh,Pygal,hvplot,holoviews,chartify,lets-plot,pyqtgraph,glue,plotnine,pygal,bqplot,toyplot,chart,itkwidgets,vedo,ipyvolume,pyvista,glumpy,geopandas,pycountry,geopy,geo-py,pypopulation, geotext,folium,cartopy,gmplo,ipyleaflet,geoviews,geoplot,splot,arviz, hypertools,geoplotlib,Geopandas package,choroplethmaps,Leafmap,Dash,Pydot,Geoplotlib,ggplot,visualizer,Greppo,Altair,folium,geoplot,networkx,graphviz,pydot,pygraphviz,python-igraph,pyvis,pygsp,ipycytoscape,nxviz ipydagred3,Diffbot,etc...)
Dashboarding : bokeh,dash,streamlit,panel,visdom ,voila,wave,jupyter-flex,ipyflex,pandas_bokeh
Openpxl: Automate Excel Reporting Datapane: A Python Library to Build Interactive Reports
Scatterplot,Binned Scatterplot,multi line plot,bubble chart,line charts,bar chart,histogram,boxplot, Pie charts,Line Plot,distplot,Histogram
Gantt Chart,bubble charts,area plot,heat map,index plot,violin plot,time series plot,density plot,dot plot,strip plot,plotly,Choropleth Map,Kepler,PDF,Kernel density function,networkx,Scatter_matrix,Bootstrap_plot,functionvis,Higher-Dimensional Plots,3-D Plots,3D Plots With Matplotlib,3D Plots With Plotly,Animated Plot With Plotly,Word Clouds,HoloViz,Horizontal Bar Graphs,Stacked Bar Graphs,Group Bar Graphs,Raincloud Plotsradviz,bootstrap_plot,lag_plot,JoyPy plots,Gantt Chart,Box and Whisker Plot,Waterfall Chart,Pictogram Chart,Timeline,highlight Table,Bullet Graph,Choropleth Map,Word Cloud,Network Diagram,Correlation Matrices,Bubble clouds,Cartograms,Circle views,Dendrograms,Dot distribution maps,Open-high-low-close charts,Polar areas,Radial trees,Ring Charts,Sankey diagram,Span charts,Streamgraphs,Treemaps,Wedge stack graphs, table charts,lollipop charts,distplot,floWeaver
hvplot A high-level plotting API for the PyData ecosystem built on HoloViews https://hvplot.holoviz.org/
50-charts https://towardsdatascience.com/how-did-i-classify-50-chart-types-by-purpose-a6b0aa5b812d
all in one https://app.learney.me/
Python Tool For Visualizing and Plotting 2D/3D Scientific Data https://analyticsindiamag.com/guide-to-mayavi-a-python-tool-for-visualizing-and-plotting-2d-3d-scientific-data/
patchworklib - combine multiple py charts easily
7 Techniques to Visualize Geospatial Data https://www.kdnuggets.com/2017/10/7-techniques-visualize-geospatial-data.html
data to viz https://www.data-to-viz.com/
Interactive plots directly with pandas https://towardsdatascience.com/get-interactive-plots-directly-with-pandas-13a311ebf426
Top 10 Data Visualization Tools https://www.analyticsvidhya.com/blog/2021/04/top-10-data-visualization-tools/ https://www.xenonstack.com/blog/data-visualization-tools/
https://www.analyticsvidhya.com/blog/2021/03/when-to-use-what-plot-a-beginners-guide-to-select-plots-for-visualization/
https://towardsdatascience.com/8-free-tools-to-make-interactive-data-visualizations-in-2021-no-coding-required-2b2c6c564b5b
https://datavizproject.com/ https://datavizcatalogue.com/
https://attachments.convertkitcdnm.com/232198/ee18f415-1406-4e5c-94f1-49a2c6e3ec4e/Statistics-The-Big-Picture-Poster.pdf
https://towardsdatascience.com/8-free-tools-to-make-interactive-data-visualizations-in-2021-no-coding-required-2b2c6c564b5b
HiPlot (high dimensional data)-https://github.com/facebookresearch/hiplot https://levelup.gitconnected.com/learn-hiplot-in-6-mins-facebooks-python-library-for-machine-learning-visualizations-330129d558ac
https://towardsdatascience.com/top-6-python-libraries-for-visualization-which-one-to-use-fe43381cd658
https://www.kaggle.com/abhishekvaid19968/data-visualization-using-matplotlib-seaborn-plotly
𝗞𝗲𝗿𝗮𝘀 𝗠𝗼𝗱𝗲𝗹 𝘃𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗼𝗿(ann-visualizer)- 𝗽𝗶𝗽𝟯 𝗶𝗻𝘀𝘁𝗮𝗹𝗹 𝗴𝗿𝗮𝗽𝗵𝘃𝗶𝘇
univariate and bivariate and multivariate analysis
model visualization Tensorboard,netron,playground tensorflow,plotly,TensorDash,Dash,Microscope,Lucid
distributions(discerte,continous)
data distributions-normal distribution,Standard Normal Distribution,Student's t-Distribution,Bernoulli Distribution,Binomial Distribution,Poisson Distribution,Uniform Distribution,F Distribution,Covariance and Correlation
Pingouin statistical package https://pingouin-stats.org/index.html https://www.youtube.com/watch?v=zqi51Wu5qC0
Types of Statistics
1.Descriptive
Descriptive statistics :Mean, mode, standard deviation, median ,absolute deviation, kurtosis, skewness
2.Inferential
Types of data
1) Categorical (nomial,ordinal)
2) Numerical (discerte,continous)
random variable(discerte random variable ,continous random variable)
Quantile statistics Q1, Q2, Q3, min, max, range, interquartile range
Central Limit Theorem,Bayes Theorem,Confidence Interval,Hypothesis Testing,z test, t test,f test,Confidence Interval,1 tail test, 2 tail test,chisquare test,anova test,A/B testing
Categorical vs Categorical Chi-square test,Information gain,Cramer’s V
Categorical vs Numerical Student T-test,ANOVA,Logistic regression,Discretize Y (left column),Point-biserial correlation
Numerical vs Categorical Student T-test,ANOVA,Logistic regression,Discretize X (row above)
Numerical vs Numerical Correlation,Linear Regression,Discretize Y (left column),Discretize X (row above)
4.Feature selection https://github.com/solegalli/feature-selection-for-machine-learning
upgini Free automated data enrichment library for machine learning https://github.com/upgini/upgini https://upgini.com/
FeatureSelector https://github.com/WillKoehrsen/feature-selector feature_engine https://github.com/solegalli/feature_engine
1.Filter methods (Removing Constant feature,Removing Quasi constant feature,Removing Duplication feature,Removing Correlated Features,feature importance,chisquare test,Ttest,ftest,vif,anova test,information gain,F-score,Mutual Information,hypothesis test,information gain,Univariate Selection Methods,SelectKBest,SelectPercentile,Variance threshold,Fisher’s Score,Dispersion ratio Mean Absolute Difference (MAD), constant features elimination, quasi-constant features elimination, duplicate feature elimination,univariate method,mutual information, correlation etc...),Correlation Coefficient,Variance Threshold ,Mean Absolute Difference (MAD),Dispersion ratio,Variance inflation,factor Condition Index
2.Wrapper methods (recursive feature eliminiation,Recursive feature addition,SelectKbest,boruta,mRMR,forward feature selection,backward feature elimination,Bi-directional selection,exhaustic feature selection,stepwise selection,step forward selection,step backward selection and exhaustive search etc...)
3.Embedded method (lasso regression,ridge regression,elastic net regression,tree based(Tree-based methods like Random Forest Importance etc...),Feature Selection by Tree importance,Feature selection with decision trees,regression coefficients(logistic,linear coeffiicients),Recursive feature elimination based on importance,Least absolute deviation)
4.Hybrid Method(Recursive Feature Selection,Recursive Feature addition,Recursive feature elimination,Feature Shuffling,Feature performance,Target mean performance,Permutation importance,Population stability index,Target encoding)
unsupervised Feature selection:Principal Component Analysis,Independent Component Analysis,Non-Negative Matrix Factorization,t-distributed Stochastic Neighbor Embedding,Autoencoder
Single-Agent Reinforcement Learning Feature Selection (SARLFS) ,Multi-Agent Reinforcement Learning Feature Selection (MARLFS)
ITMO_FS is a feature selection library https://github.com/ctlab/ITMO_FS
Sparse Features - Removing features,LASSO regularization,features dense(pca,Feature hashing),Using models that are robust to sparse features
5.Feature creation
feature selection https://medium.com/analytics-vidhya/feature-selection-extended-overview-b58f1d524c1c
mrmr_selection automatic feature selection at scale https://github.com/smazzanti/mrmr
Feature selector https://github.com/WillKoehrsen/feature-selector
Simulated Annealing https://github.com/kennethleungty/Simulated-Annealing-Feature-Selection
boruta https://github.com/scikit-learn-contrib/boruta_py https://github.com/Ekeany/Boruta-Shap
DropConstantFeatures DropDuplicateFeatures DropCorrelatedFeatures
step forward feature selection https://www.kdnuggets.com/2018/06/step-forward-feature-selection-python.html
automatic feature selection mrmr https://github.com/smazzanti/mrmr
Creating New Features Deep Feature Synthesis https://docs.featuretools.com/en/v0.16.0/automated_feature_engineering/afe.html
SequentialFeatureSelector: The popular forward and backward feature selection
Alternative feature selection methods Feature shuffling,Feature performance,Target mean performance
Automatic Feature Selection : recursive feature elimination and cross-validation
Powershap: A Shapley feature selection method https://github.com/predict-idlab/powershap
VarianceThreshold,Chi-squared stats,ANOVA using f_classif,Univariate Linear Regression Tests using f_regression,F-score vs Mutual Information,Mutual Information for discrete value,Mutual Information for continues value,SelectKBest,SelectPercentile,SelectFromModel,Recursive Feature Elimination,Extra Trees model
4.Feature Importance
a.ExtraTreesClassifier,ExtraTreesregressor
b.SelectKBest
c.Logistic Regression
d.Random_forest_importance,Permutation Feature Importance
e.decision tree
f.Linear Regression
g.xgboost
h.Pearson correlation
Forward selection,Chi-square,Logit (Logistic Regression model)
5.curse of dimensionality (as dimension increases performance decreases)
6.highly correleated features then can take any 1 feature (multicollinearity)
7.dimension reduction
8.lasso regression to penalise unimportant features
9.VarianceThreshold ,selectkbest
10.model based selection
11.Mutual Information Feature Selection
12.remove features with very low variance (quasi constant feature dropping)
13.Univariate feature selection
14.importance of feature (random forest importance)
15.feature importance with decision trees
16.PyImpetus
17.drop constant features (variance=0) , Drop Highly Correlated Features
18.variance inflation factor(vif)
19.Recursive Feature Elimination RecursiveFeatureAddition
20.exchaustive feature selection
21.Statistical Methods , Hypothesis Testing ,Recursive Feature Elimination
22.Boruta https://github.com/scikit-learn-contrib/boruta_py https://analyticsindiamag.com/hands-on-guide-to-automated-feature-selection-using-boruta/
23.Sequence Feature Selection, SelectFromModel
Missing Value Ratio Analysis,Low Variance Filter,High Correlation Filter,Backward Feature Elimination,Forward Feature Elimination ,SequentialFeatureSelector
PyImpetus https://github.com/atif-hassan/PyImpetus
https://www.analyticsvidhya.com/blog/2016/12/introduction-to-feature-selection-methods-with-an-example-or-how-to-select-the-right-variables/
Automate your Feature Selection Workflow in one line of Python code https://github.com/AutoViML/featurewiz https://towardsdatascience.com/automate-your-feature-selection-workflow-in-one-line-of-python-code-3d4f23b7e2c4
https://machinelearningmastery.com/feature-selection-with-real-and-categorical-data/ https://machinelearningmastery.com/statistical-hypothesis-tests-in-python-cheat-sheet/
https://www.analyticsvidhya.com/blog/2020/10/a-comprehensive-guide-to-feature-selection-using-wrapper-methods-in-python/
https://towardsdatascience.com/5-feature-selection-method-from-scikit-learn-you-should-know-ed4d116e4172
Feature Engineering Tools https://neptune.ai/blog/feature-engineering-tools?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-feature-engineering-tools
https://towardsdatascience.com/practical-code-implementations-of-feature-engineering-for-machine-learning-with-python-f13b953d4bcd
PyRasgo https://github.com/rasgointelligence/PyRasgo https://docs.rasgoml.com/rasgo-docs/?_ga=2.209281745.2123722956.1645542654-525286113.1645542654
Automated Feature Engineering Using Deep Feature Synthesis (DFS) https://heartbeat.comet.ml/introduction-to-automated-feature-engineering-using-deep-feature-synthesis-dfs-3feb69a7c00b
Automatic Feature Selection in python https://verstack.readthedocs.io/en/latest/#featureselector
rulefit https://github.com/christophM/rulefit
Featurewiz: Fast way to select the best features in a data
select best features featurewiz https://github.com/AutoViML/featurewiz
Featuretools: https://github.com/alteryx/featuretools https://analyticsindiamag.com/introduction-to-featuretools-a-python-framework-for-automated-feature-engineering/
AutoFeat: https://github.com/cod3licious/autofeat
TSFresh: https://github.com/blue-yonder/tsfresh
FeatureSelector: https://github.com/WillKoehrsen/feature-selector
unsupervised feature selection technique https://github.com/atif-hassan/FRUFS
rulefit https://github.com/christophM/rulefit
5.Data splitting
Splitting ratio of data deponds on size of dataset available
Training data,Validation data,Testing data
6.Model selection
Machine learning https://scikit-learn.org/stable/index.html
Choose the Right Machine Learning Algorithm for Your Application https://towardsdatascience.com/how-to-choose-the-right-machine-learning-algorithm-for-your-application-1e36c32400b9
Time Complexity Of Machine Learning Models -https://www.thekerneltrip.com/machine/learning/computational-complexity-learning-algorithms/
interactive tools https://github.com/Machine-Learning-Tokyo/Interactive_Tools
mindsdb In-Database Machine Learning https://github.com/mindsdb/mindsdb
HTML tables into Google Sheets -https://towardsdatascience.com/import-html-tables-into-google-sheets-effortlessly-f471eae58ac9
Machine Learning Playground https://ml-playground.com/
visual introduction to machine learning http://www.r2d3.us/visual-intro-to-machine-learning-part-1/
draw a dataset from inside jupyter https://pypi.org/project/drawdata/ https://www.youtube.com/watch?v=b0rsDPQ3bjg
Visual programming language for machine learning - Kobra https://kobra.dev/
compose generate labels for supervised learning https://github.com/alteryx/compose https://analyticsindiamag.com/guide-to-prediction-engineering-with-compose/
human-learn https://towardsdatascience.com/human-learn-create-rules-by-drawing-on-the-dataset-bcbca229f00
Microscope https://microscope.openai.com/models https://www.youtube.com/watch?v=y0-ISRhL4Ks
Ptpython Autocompletion, Autosuggestion, Docstring https://github.com/prompt-toolkit/ptpython https://towardsdatascience.com/ptpython-a-better-python-repl-6e21df1eb648
3 Tools to Track and Visualize the Execution of your Python Code https://towardsdatascience.com/3-tools-to-track-and-visualize-the-execution-of-your-python-code-666a153e435e
ML Code memory Consuming https://towardsdatascience.com/how-much-memory-is-your-ml-code-consuming-98df64074c8f
PyGrid Privacy-preserving, Decentralized Data Science https://github.com/OpenMined/PyGrid/
Best and Worst Cases of Machine-Learning Models https://medium.com/towards-artificial-intelligence/best-and-worst-cases-of-machine-learning-models-part-1-36cdb9296611
https://www.youtube.com/watch?v=mlumJPFvooQ&list=PLZoTAELRMXVM0zN0cgJrfT6TK2ypCpQdY
skater Machine Learning Model Interpretation https://towardsdatascience.com/machine-learning-model-interpretation-47b4bc29d17f
Speedml Speeding up Machine Learning https://towardsdatascience.com/speedml-speeding-up-machine-learning-5dccbf21effd
2-2000x faster ML algos https://github.com/danielhanchen/hyperlearn
snapml 30 Times Faster Than Scikit-Learn snapml https://www.zurich.ibm.com/snapml/
scikit-learn-intelex https://github.com/intel/scikit-learn-intelex
composer speed-up algorithms for model training https://github.com/mosaicml/composer
pdpipe https://github.com/pdpipe/pdpipe pipeline https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html
PHOTONAI A high level Python API for designing and optimizing machine learning pipelines https://www.photon-ai.com/
Machine Learning in Tableau with PyCaret https://towardsdatascience.com/machine-learning-in-tableau-with-pycaret-166ffac9b22e
TabNet balances explainability and model performance on tabular data https://towardsdatascience.com/tabnet-e1b979907694
FreaAI That Automatically Finds Weaknesses In ML models https://analyticsindiamag.com/ibm-launches-freaai-that-automatically-finds-weaknesses-in-ml-models/
A.Supervised learning (have label data)
Transformers for Tabular Data: TabTransformer https://github.com/lucidrains/tab-transformer-pytorch
1.Regression (output feature in continous data form)
linear regression,Multiple Linear Regression,polynomial regression,Exponential Regression,Bayesian Regression,Robust Regression,Huber regressor,support vector regression,Decision Tree Regression,Random Forest Regression,TensorFlow Decision Forests,RANSAC Regression,
least square method,linear-tree,Random Forest Regression, Regularized Greedy Forests,xgboost,ridge(L2 Regularization),lasso(L1 Regularization (more sparse)),elastic, Lars,catboost,gradientboosting,adaboost,Explainable Boosting Machine,Histogram-Based Gradient Boost,Stacked Gradient Boosting Machines,LightBoost,CatBoost, XGBoost,autoxgb,NGBoost,XBNet,Chefboost,GPBoost,Local Cascade Ensemble,Principal Component Regression,huber_regression,ransac_regression,theilsen_regression,Linear spline,Isotonic regression,Bin regression,Cubic spline,Natural cubic splin,Exponential moving average,Quantile Regression,Quantile Random Forests,Quantile GBM
elsatic net,light gbm,ordinary least squares,cart,Stepwise Regression,Multivariate Adaptive Regression Splines ,Generalised Additive Model(learn non-linear feature),tabnet,Linear Tree regression
statsassume Automating Assumption Checks for Regression Models https://github.com/kennethleungty/statsassume
Locally Weighted Linear Regression https://towardsdatascience.com/locally-weighted-linear-regression-in-python-3d324108efbf
TuringBot https://www.youtube.com/watch?v=LyKzKvjyIPo
chefboost is an alternative library for training tree-based models https://github.com/serengil/chefboost
growtrees About Cost-Aware Robust Tree Ensembles for Security Applications https://github.com/surrealyz/growtrees
2.Classification (output feature in categorical data form)
Binary,Multi-class,Multi-labe
Logistic Regression,K-Nearest Neighbors,Support Vector Machine,Kernel SVM,Naive Bayes,Decision Tree Classification,linear-tree,TensorFlow Decision Forests,
Random Forest Classification,TensorFlow Decision Forests, Regularized Greedy Forests,xgboost,DART booster,autoxgb,LightGBM,adaboost,Gradient Boost,XBNet,catboost,gaussian NB,LGBMClassifier,LinearDiscriminantAnalysis, Extreme Gradient Boosting Machine, Explainable Boosting Machine,fairgbm
,Chefboost,GPBoost,NGBoost,Local Cascade Ensemble,passive aggressive classifier algorithm,cart,c4.5,c5.0,tabnet,ExtraTreesClassifier,TabPFN
https://mlwhiz.com/blog/2019/11/12/dtsplits/?utm_campaign=the-simple-math-behind-3-decision-tree-splitting-criterions&utm_medium=social_link&utm_source=missinglettr-linkedin
4 Useful techniques avoid overfitting in decision trees https://towardsdatascience.com/4-useful-techniques-that-can-mitigate-overfitting-in-decision-trees-87380098bd3c
Machine Learning – it’s all about assumptions https://www.kdnuggets.com/2021/02/machine-learning-assumptions.html
GPBoost: A Library To Combine Tree-Boosting With Gaussian Process And Mixed-Effects Models https://analyticsindiamag.com/guide-to-gpboost-a-machine-learning-library-to-combine-tree-boosting/
Data and Concept Drift https://evidentlyai.com/blog/machine-learning-monitoring-data-and-concept-drift
B.Unsupervised learning(no label(target) data)
1.Dimensionality reduction - PCA,ppa,SVD,LDA,som,tsne,openTSNE,plsr,pcr,autoencoders,kernelpca,Latent Semantic Analysis,Factor Analysis,Locality Preserving Projections,Isometric Mapping,Multiple correspondence analysis (MCA),Multiple factor analysis (MFA),Factor analysis of mixed data (FAMD),vae,CompressionVAE,Gaussian Mixture Model,Bayesian Gaussian Mixture Model
non-linear data using Kernel PCA, Non-Negative Matrix Factorization(NMF), IsoMap, t-SNE, and UMAP,TDA(Topological Data Analysis)
t-SNE Effectively https://distill.pub/2016/misread-tsne/
2.Clustering : Centroid-based Model ,Density-based Model ,Distribution-based Model,Connectivity-based model
17 clustering https://towardsdatascience.com/17-clustering-algorithms-used-in-data-science-mining-49dbfa5bf69a
https://neptune.ai/blog/clustering-algorithms?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-clustering-algorithms
classix Fast and explainable clustering based on sorting https://github.com/nla-group/classix
https://www.mygreatlearning.com/blog/unsupervised-machine-learning/?highlight=unsupervised%20machine%20learning&utm_source=GLA&utm_medium=Blog&utm_campaign=1-16th%20May
https://scikit-learn.org/stable/modules/clustering.html https://machinelearningmastery.com/clustering-algorithms-with-python/
https://towardsdatascience.com/17-clustering-algorithms-used-in-data-science-mining-49dbfa5bf69a
RFM Segmentation in E-Commerce https://towardsdatascience.com/rfm-segmentation-in-e-commerce-e0209ce8fcf6
kmodes https://www.youtube.com/watch?v=8eATPLDJ0NQ
Agglomerative Hierarchical Clustering Using AGNES Algorithm https://analyticsindiamag.com/perform-agglomerative-hierarchical-clustering-using-agnes-algorithm/
CLARANS Clustering Algorithm https://analyticsindiamag.com/comprehensive-guide-to-clarans-clustering-algorithm/
https://pub.towardsai.net/fully-explained-birch-clustering-for-outliers-with-python-2ad6243f126b
https://www.kdnuggets.com/2020/12/algorithms-explained-k-means-k-medoids-clustering.html
https://www.kdnuggets.com/2017/03/naive-sharding-centroid-initialization-method.html
CLASSIX clustering https://github.com/nla-group/classix
K-Means 8x faster, 27x lower error than Scikit-learn in 25 lines https://www.kdnuggets.com/2021/01/k-means-faster-lower-error-scikit-learn.html#.YAHAAIpnx4A.linkedin
k-Means Clustering by up to 10x Over Scikit-Learn https://towardsdatascience.com/how-to-speed-up-your-k-means-clustering-by-up-to-10x-over-scikit-learn-5aec980ebb72
3.Association Rule Learning - support,lift,confidence,leverage,Conviction,aprior,elcat,Fp-growth,Fp-tree construction,FP-Max Algorithm,association_rules,Frequent Itemset Mining,Multi-Relation Association Rules,High-order pattern discovery,K-optimal pattern discovery,Approximate Frequent Itemset,Generalized Association Rules,Quantitative Association Rules,Interval Data Association Rules,Sequential pattern mining,Hypergeometric Networks,Constraint Based Mining,Multi-level Association Rules,Fuzzy Association Rules
Sequential Patterns
Generalized Sequential Patterns (GSP)
Prefix-Projected Sequential Pattern Mining (PrefixSpan)
Sequential Pattern Discovery using Equivalent Class (SPADE)
Frequent Pattern-Projected Sequential Pattern Mining (FreeSpan)
interpretable association rule https://analyticsindiamag.com/a-guide-to-interpretable-association-rule-mining-using-pycaret/
4.Market Segmentation
Demographic Segmentation,Geographic segmentation,Firmographic segmentation,Behavioural segmentation,
4.Recommendation system - Surprise,TensorFlow Recommendation,Recmterics
competitive-recsys https://github.com/chihming/competitive-recsys
a.collaborative Recommendation system (model based, memory based(item based,user based),hybrid) user-item interaction matrix
Classification-based collaborative filtering
Model-based collaborative filtering systems(Cluster model,linear regression,Bayesian networks ,latent factor(probabilistic latent,matrix factorization(als,SGD,SVD),neural network,lda))
b.content based Recommendation system
similarity based(user-user similarity,item-item similarity)
matrix factorization(SVD and SVD++),Popularity-based recommenders
c.utility based Recommendation system
d.knowledge based Recommendation system
e.demographic based Recommendation system
f.hybrid based Recommendation system
Popularity based Recommendation system (NON-PERSONALIZED )
g.Average Weighted Recommendation
h.using K Nearest Neighbor
i.cosine distance recommender system
item2vec
j.TensorFlow Recommenders https://www.tensorflow.org/recommenders
recommenders https://github.com/microsoft/recommenders
Neural Collaborative Filtering for Personalized Ranking
AutoRec: Rating Prediction with Autoencoders Matrix Factorization
k.suprise baseline model
Context-aware Recommender Systems,Mobile Recommender Systems,Group Recommender Systems,Multi-stakeholder Recommender Systems
l.Neural Collaborative Filtering (NCF)
l.Tf-Rec TensorFlow Recommendation https://github.com/Praful932/Tf-Rec
Nvidia Merlin
m.Deep Learning Recommendation Models https://www.kdnuggets.com/2021/04/deep-learning-recommendation-models-dlrm-deep-dive.html
Restricted Boltzmann Machines,Auto-Encoders
TOROS Buffalo https://github.com/kakao/buffalo
recommenders-https://github.com/microsoft/recommenders
LightFM https://making.lyst.com/lightfm/docs/home.html
lkpy Python recommendation toolkit https://github.com/lenskit/lkpy https://analyticsindiamag.com/how-to-build-recommender-systems-using-lenskit/
torchrec https://github.com/pytorch/torchrec
PyTorch implementations of deep reinforcement learning algorithms and environments https://github.com/p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch
recmetrics library of metrics for evaluating recommender systems https://github.com/statisticianinstilettos/recmetrics
Downsize Recommendation Models By 112 Times https://analyticsindiamag.com/explained-facebooks-novel-method-to-downsize-recommendation-models-by-112-times/
torchrec,Lenskit,RGRecSys,Surprise,Tensorflow Recommenders,NVIDIA-Merlin,Recmetrics,Surprise,DeepCTR,OpenRec,fastFM,LightFM
Session-based RecSys could be done with:Recency-based Weighting (exp.decay),Probabilistic Graphical Models (FPMC, FOSSIL),Convolutional NN (Caser, NextItNet),Recurrent NN (GRU4Rec),Graph NN (SRGNN, GCSAN),Attention(STAMP, NARM, FDSA, SHAN),Transformer(BERT4Rec, Transformer4Rec),Knowledge Graph(KSR, GRU4RecKG, KGCN, KGAT, RippleNet),Landscape, Rexy, Tensor Recommendation Engine, Light FM, Spotlight, Case Recommender
https://analyticsindiamag.com/top-open-source-recommender-systems-in-python-for-your-ml-project/
https://towardsdatascience.com/modern-recommender-systems-a0c727609aa8
https://machinelearningmastery.com/recommender-systems-resources/
C.Ensemble methods
1.Stacking models https://www.analyticsvidhya.com/blog/2021/03/advanced-ensemble-learning-technique-stacking-and-its-variants/?
vecstack https://github.com/vecxoz/vecstack
Cascading Ensembles,Cohorted Ensembles
2.Bagging models (Bagging (with the replacement) , Pasting ( without replacement ))
3.Boosting models
4.Blending
5.Voting (Hard Voting,Soft Voting)
VOTING ENSEMBLE
Simple : Max Voting, Averaging, Weighted Averaging,Simple Average,Rank Averaging,Bayesian Model,Majority Voting
mlens ML-Ensemble – high performance ensemble learning https://github.com/flennerhag/mlens
https://analyticsindiamag.com/do-ensemble-methods-always-work/
Shapley value of players (models) in weighted voting games https://github.com/benedekrozemberczki/shapley
D.Reinforcement learning https://neptune.ai/blog/best-reinforcement-learning-tutorials-examples-projects-and-courses
2 types a)model free b)model based
gym-https://github.com/openai/gym reinforcement learning by using PyTorch-https://github.com/SforAiDl/genrl
agent,environment,policy(On-Policy vs Off-Policy),reward function,value function,state,action,episode,actor-critic
agent apply action to environment get corresponding reward so that it learn environment
How to get started with Reinforcement Learning https://gordicaleksa.medium.com/how-to-get-started-with-reinforcement-learning-rl-4922fafeaf8c
1.Q-Learning
2.Deep Q-Learning
3.Deep Convolutional Q-Learning
Deep Deterministic Policy Gradient
4.Twin Delayed DDPG,DQN,Temporal difference
5.A3C (Actor Critic) ,A2C, Soft Actor Critic (SAC),Adversarial Motion Priors (AMP),Cross-Entropy Method (CEM),Deep Deterministic Policy Gradient (DDPG),Double Deep Q-Network (DDQN),Deep Q-Network (DQN),Proximal Policy Optimization (PPO),Q-learning (Q-learning),Soft Actor-Critic (SAC),State Action Reward State Action (SARSA),Twin-Delayed DDPG (TD3),Trust Region Policy Optimization (TRPO)
6.Advantage weighted actor critic (AWAC).
7.XCS
8.genetic algorithm,sarsa,natural policy gradient,Policy Gradient Learning
https://simoninithomas.github.io/deep-rl-course/
SARSA,REINFORCE,PPO,DDPG,Ddpg,TD3
AUTORL: AUTOML FOR RL https://www.automl.org/blog-autorl/
Environments-OpenAI Gym, DeepMind Lab, Unity ML-Agents
https://data-flair.training/news/python-libraries-for-reinforcement-learning/
https://analyticsindiamag.com/8-best-free-resources-to-learn-deep-reinforcement-learning-using-tensorflow/
https://analyticsindiamag.com/top-8-autonomous-driving-open-source-projects-one-must-try-hands-on/
https://analyticsindiamag.com/8-toolkits-for-reinforcement-learning-models-that-make-reasoning-explainability-core-to-ai/
https://neptune.ai/blog/best-reinforcement-learning-tutorials-examples-projects-and-courses
https://towardsdatascience.com/value-based-methods-in-deep-reinforcement-learning-d40ca1086e1
https://neptune.ai/blog/best-reinforcement-learning-tutorials-examples-projects-and-courses?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-best-reinforcement-learning-tutorials-examples-projects-and-courses
TensorForce: A TensorFlow-based Reinforcement Learning Framework https://analyticsindiamag.com/guide-to-tensorforce-a-tensorflow-based-reinforcement-learning-framework/
Decision Transformer: Reinforcement Learning via Sequence Modeling https://github.com/kzl/decision-transformer
Open AI Gym - https://gym.openai.com/
DeepMind’s MuZero https://deepmind.com/blog/article/muzero-mastering-go-chess-shogi-and-atari-without-rules?utm_campaign=Learning%20Posts&utm_content=150411901&utm_medium=social&utm_source=twitter&hss_channel=tw-3018841323
KerasRL https://github.com/keras-rl/keras-rl
pyqlearning
tensorforce https://tensorforce.readthedocs.io/en/latest/index.html
Practical_RL https://github.com/yandexdataschool/Practical_RL
rl_coach https://github.com/IntelLabs/coach#installation MushroomRL https://mushroomrl.readthedocs.io/en/latest/
TFAgents https://github.com/tensorflow/agents (https://www.tensorflow.org/agents) https://deepmind.com/blog/article/trfl
TorchRec https://pytorch.org/blog/introducing-torchrec/ TensorFlow Recommenders https://www.tensorflow.org/recommenders
behaviour trees used in reinforcement learning https://analyticsindiamag.com/how-are-behaviour-trees-used-in-reinforcement-learning/
Automate The Stock Market Using FinRL (Deep Reinforcement Learning Library) https://analyticsindiamag.com/stock-market-prediction-using-finrl/
Stable Baselines https://github.com/openai/baselines
https://www.youtube.com/playlist?list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc
https://neptune.ai/blog/the-best-tools-for-reinforcement-learning-in-python?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-the-best-tools-for-reinforcement-learning-in-python
Semi-Supervised Learning-small amount of labeled data with a large amount of unlabeled data during training
Machine Learning with Graphs http://web.stanford.edu/class/cs224w/
E.Deep-learning (use when have huge data and data is highly complex and state of art for unstructured data) https://www.kdnuggets.com/2019/11/designing-neural-networks.html
Model Zoo Discover open source deep learning code and pretrained models https://modelzoo.co/
Visualizing your Neural Network with Netron,Net2Vis,visualkeras,draw_convnet,NNSVG,PlotNeuralNet,Tensorboard,Caffe,Matlab,Keras.js,keras-sequential-ascii ,Netron,DotNet,Graphviz ,Keras Visualization,Conx,ENNUI,NNet,GraphCore ,Monial,Quiver
Sharing the best resources on various machine learning topics https://www.backprop.org/
deeplearning-models-https://github.com/rasbt/deeplearning-models
Deep-Learning-with-PyTorch- https://pytorch.org/assets/deep-learning/Deep-Learning-with-PyTorch.pdf
Frameworks:Pytorch,Tensorflow,Keras,caffe,theano,MXNet,Matlab,Microsoft Cognitive Toolkit,opacus(Train PyTorch models with Differential Privacy)
https://towardsdatascience.com/the-mostly-complete-chart-of-neural-networks-explained-3fb6f2367464 https://docs.deepstack.cc/getting-started/index.html
fastest way to build, debug, and interpret neural networks https://www.perceptilabs.com/
Nengo: A New Neural Network Building and Deployment Tool https://pub.towardsai.net/nengo-a-new-neural-network-building-and-deployment-tool-66677c65fa19
Binarized Neural Network memory size is reduced, and bitwise operations improve the power efficiency https://neptune.ai/blog/binarized-neural-network-bnn-and-its-implementation-in-ml
paddlehub https://github.com/PaddlePaddle/PaddleHub Performing Computer Vision & NLP Tasks in a Single Of Code https://towardsdatascience.com/performing-computer-vision-nlp-tasks-in-a-single-of-code-f7205f212d34
scikit-neuralnetwork https://towardsdatascience.com/the-simplest-way-to-train-a-neural-network-in-python-17613fa97958 https://github.com/aigamedev/scikit-neuralnetwork
NVIDIA’s Kaolin: A 3D Deep Learning Library https://analyticsindiamag.com/nvidias-kaolin-3d-deep-learning-library/ https://github.com/NVIDIAGameWorks/kaolin
PySyft is a Python library for secure and private Deep Learning https://github.com/OpenMined/PySyft
keras-vis Visualizing Learning of a Deep Neural Network https://towardsdatascience.com/deep-learning-model-visualization-6cf6290dc981
Deep Replay Visualizing Learning of a Deep Neural Network https://towardsdatascience.com/visualizing-learning-of-a-deep-neural-network-b05f1711651c
keras-visualizer Visualizing Keras Models https://towardsdatascience.com/visualizing-keras-models-4d0063c8805e
Lucid Library is an open source framework to improve the interpretation of deep neural networks
Gradient-Centralization-TensorFlow improve your training performance of TensorFlow models with just 2 lines of code! https://github.com/Rishit-dagli/Gradient-Centralization-TensorFlow
XBNet: An Extremely Boosted Neural Network
MIL-WebDNN Fastest DNN Execution Framework on Web Browser https://mil-tokyo.github.io/webdnn/
Vector Hub models to turn data into vectors text2vec, image2vec, video2vec, graph2vec, bert, inception, etc https://github.com/RelevanceAI/vectorhub
torchbearer: A model fitting library for PyTorch https://github.com/pytorchbearer/torchbearer
1.Multilayer perceptron(MLP)
1.Regression task
2.Classification task
Tabnet and deep tables for tabular dataset using deep learning
2.Convolutional neural network ( use for image data)
Best MLOps Tools for Your Computer Vision Project Pipeline https://neptune.ai/blog/best-mlops-tools-for-computer-vision-project?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-best-mlops-tools-for-computer-vision-project
mediapipe https://google.github.io/mediapipe/ cv modelhub https://modelplace.ai/
all openmmlab https://github.com/open-mmlab mmdetection,mmsegmentation,mmediting,mmdetection3d,mmaction2,mmocr,mmpose,etc...
glasses High-quality Neural Networks for Computer Vision https://github.com/FrancescoSaverioZuppichini/glasses
IceVision https://airctic.com/0.8.0/
Top Computer Vision Google Colab Notebooks- https://www.qblocks.cloud/creators/computer-vision-google-colab-notebooks
for low code object detection (detecto)- https://github.com/alankbi/detecto
CV-pretrained-model- https://github.com/balavenkatesh3322/CV-pretrained-modelCV-pretrained-model-
Fast Computer Vision Model Building PyTorch Lightning Flash and FiftyOne https://towardsdatascience.com/open-source-tools-for-fast-computer-vision-model-building-b39755aab490
5 Open-Source Facial Recognition https://medium.com/analytics-vidhya/ways-to-boost-your-computer-vision-projects-by-using-5-open-source-facial-recognition-projects-56668f170cb9
cnn alternative CapsNet https://github.com/XifengGuo/CapsNet-Keras
EDA for image data data-gradients
1.Classification of image
albumentations https://github.com/albumentations-team/albumentations AugLy https://github.com/facebookresearch/AugLy
create own model,Lenet,Alexnet,DenseNet,MobileNet,ShuffleNet,SqueezeNet,Resenet,GoogleNet,Inception,Vgg16,vgg19,Efficient,EfficientNetV2,EfficientDet,residualnet,Nasnet,STN,nasneta,senet,amoebanetc,DeiT (tiny,small,base),Meta Pseudo Labels,res-mlp-pytorch,MLP-Mixer,vit,DynamicViT, FNet,gMLP models,nfnet
mmclassification https://github.com/open-mmlab/mmclassification
https://theaisummer.com/cnn-architectures/ https://paperswithcode.com/sota/image-classification-on-imagenet
timm https://pypi.org/project/timm/ https://github.com/rwightman/pytorch-image-models
2.Localization of object in image
3.Object detection and object segmentation
rcnn,fastrcnn,fastercnn,TensorFlow Object Detection,yolo v1,yolo v2,yolo v3,SlimYOLOv3,yolo v4,PP-YOLO,scaled yolov4,YOLOR,YoloV5,YOLOS,efficinetdet,fast yolo,yolo tiny,yolo lite,yolo tiny++,yolo act++,yolonas,yolov8
maskrcnn,DeepLab-v3-plus,ssd,detectron,detectron2,D2Go,mobilenet,retinanet,R-fcn,Libra_R-CNN,detr facebook,mdetr,pspnet,segnet,U-net,UNet++,Efficient U-Nets, 𝗗𝗲𝗻𝘀𝗲-𝗚𝗮𝘁𝗲𝗱 𝗨-𝗡𝗲𝘁, nnU-Net,v-net,TransUNet, H-DenseUNet, MultiResUNet ,deeplab,globalconvolutionnetwork,fcn,EfficientDet,Vision Transformer,deit,VarifocalNet (VF-Net),DINO,BodyPix,vit,AugFPN,mlsd
PixelLib Simplifying Object Segmentation with PixelLib Library https://github.com/ayoolaolafenwa/PixelLib
mmdetection https://github.com/open-mmlab/mmdetection https://towardsdatascience.com/mmdetection-tutorial-an-end2end-state-of-the-art-object-detection-library-59064deeada3 https://github.com/open-mmlab/mmrotate
mmdetection3d https://github.com/open-mmlab/mmdetection3d mmsegmentation https://github.com/open-mmlab/mmsegmentation
fewshot https://github.com/open-mmlab/mmfewshot
Zero-Shot Object Detection , annotate dataset https://github.com/microsoft/GLIP
imageai.Detection ObjectDetection Segmentation models https://github.com/qubvel/segmentation_models
Image-Segmentation-Using-Pixellib
IceVision https://airctic.com/0.8.0/
Image Generation Using TensorFlow Keras https://analyticsindiamag.com/getting-started-image-generation-tensorflow-keras/
Video Understanding https://towardsdatascience.com/video-understanding-made-simple-with-pytorch-video-and-lightning-flash-c7d65583c37e
Getting Started With Object Detection Using TensorFlow https://analyticsindiamag.com/object-detection-using-tensorflow/
Instance Segmentation using Mask-RCNN with PixelLib and Python https://www.youtube.com/watch?v=i_-ud01wFhc
MLP MLP solution for Vision, from Google AI https://github.com/lucidrains/mlp-mixer-pytorch
MMDetection https://analyticsindiamag.com/guide-to-mmdetection-an-object-detection-python-toolbox/ mediapipe https://github.com/google/mediapipe
SSL Framework For Object Detection https://analyticsindiamag.com/googles-stac-ssl-framework-for-object-detection/
GSDT https://analyticsindiamag.com/gsdt-gnns-for-simultaneous-detection-and-tracking/
D2Go Brings Detectron2 To Mobile https://analyticsindiamag.com/facebooks-d2go-brings-detectron2-to-mobile/
AdelaiDet open source toolbox for multiple instance-level detection and recognition tasks https://github.com/aim-uofa/AdelaiDet
3d object detection https://omdena.com/blog/3d-object-detection/?utm_source=linkedin&utm_medium=organic&utm_campaign=blog&utm_term=google-analytics
PyMAF https://analyticsindiamag.com/guide-to-pymaf-pyramidal-mesh-alignment-feedback/
3 kind of object segmentation are available semantic segmentation,instance segmentation,panoptic segmentation
segmentation_models https://github.com/qubvel/segmentation_models
https://analyticsindiamag.com/guide-to-panoptic-segmentation-a-semantic-instance-segmentation-approach/ https://analyticsindiamag.com/semantic-vs-instance-vs-panoptic-which-image-segmentation-technique-to-choose/
ResNeSt: A Better ResNet with the Same Costs https://analyticsindiamag.com/guide-to-resnest-a-better-resnet-with-the-same-costs/
PAN: Pyramid Attention Network for Semantic Segmentation https://medium.com/mlearning-ai/review-pan-pyramid-attention-network-for-semantic-segmentation-semantic-segmentation-8d94101ba24a
PyTorch based low code object detection-https://github.com/alankbi/detecto
https://www.kdnuggets.com/2021/03/extraction-objects-images-videos-5-lines-code.html
autogluon
GluonCV https://medium.com/apache-mxnet/start-fitting-cv-models-like-scikit-learn-with-gluoncv-0-10-931ff910a38
https://awesomeopensource.com/project/hoya012/deep_learning_object_detection
4.objecttracking (mean shit and optical flow and kalman filter)
Tracktor++,Trackrcnn,Jde,DeepSORT,FairMOT
mmtracking https://github.com/open-mmlab/mmtracking https://github.com/open-mmlab/mmflow
mmhuman3d https://github.com/open-mmlab/mmhuman3d
Video Understanding https://github.com/open-mmlab/mmaction2
5.Deepdream,Neural style transfer, Pose estimation
generative models https://github.com/open-mmlab/mmgeneration
Machine Learning for Art https://ml4a.net/#
Pose estimation by mediapipe library https://google.github.io/mediapipe/ https://www.youtube.com/watch?v=brwgBf6VB0I
posemodule https://www.youtube.com/watch?v=5kaX3ta398w Pose Tracking https://www.youtube.com/watch?v=0JU3kpYytuQ&t=1650s
6.DEEP LEARNING METHODS FOR 2D :OpenPose,DeepPose,AlphaPose,tfpose,MultiPoseNet,AlphaPose,Movenet lighting,VIBE,DeeperCut,Mask RCNN,DeepCut,Convolutional Pose Machines,PoseNet,MoveNet,Adobe’s BodyNet,MoveNet and TensorFlow.js,High-Resolution Net,Blaze pose,Deep Pose,PoseNet
openpose wrnchai densepose
mmpose https://github.com/open-mmlab/mmpose
Pose Estimation using OpenCV https://www.analyticsvidhya.com/blog/2021/05/pose-estimation-using-opencv/
https://medium.com/beyondminds/an-overview-of-human-pose-estimation-with-deep-learning-d49eb656739b
3D POSE ESTIMATION
3D Image Classification https://keras.io/examples/vision/3D_image_classification/
TensorFlow 2 Object Detection API tutorial https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/
https://blog.paperspace.com/how-to-train-scaled-yolov4-object-detection/
Image DA libraries – Augmentor, Albumentations, ImgAug, AutoAugment, Transforms https://neptune.ai/blog/data-augmentation-in-python
Simple transformations-Resize,Gray Scale,Normalize,Random Rotation,Center Crop,Random Crop,Gaussian Blur
Position augmentation-Scaling,Cropping,Flipping,Padding,Rotation,Translation,Affine transformation,Kernel filters
Color augmentation-Brightness,Contrast,Saturation,Hue
Deep learning approach-Adverserial training,Neural style transfer,Gan data argumentation
AS-One Run YOLOv7,v6,v5,R,X in under 20 lines of code https://github.com/augmentedstartups/AS-One
Data augmentation feature space : noise,interpolation Data Space Character Level : Noise Induction,Rule-based Transformations Word Level : Noise Induction,Synonym Replacement,Embedding Replacement,Replacement by Language Models Phrase and Sentence Level : Interpolation,Structure-based Transformation Document Level:Round-trip Translation,Generative Methods
flipping, rotation, scaling ratio, noise injection, changing contrast, translation, cropping, color jittering,AutoAugment,Fast AutoAugment,Population Based Augmentation,RandAugment
More advanced techniques-Gaussian Noise,Random Blocks,Central Region
albumentations https://github.com/albumentations-team/albumentations https://towardsdatascience.com/getting-started-with-albumentation-winning-deep-learning-image-augmentation-technique-in-pytorch-47aaba0ee3f8
AugLy A Modern Data Augmentation Library https://analyticsindiamag.com/complete-guide-to-augly-a-modern-data-augmentation-library/ https://github.com/facebookresearch/AugLy
Data augmentation with tf.data
ImageGenerator image augmentation ImageDataGenerator Albumentations SOLT Imgaug Augmentor,Albumentations,Imgaug,AutoAugment (DeepAugment)
Augmentor Image augmentation library in Python for machine learning https://github.com/mdbloice/Augmentor
albumentations https://github.com/albumentations-team/albumentations
HiSD: Image-to-Image translation via Hierarchical Style Disentanglement https://analyticsindiamag.com/hisd-python-implementation-of-image-to-image-translation/
Zooming Slow-Mo https://analyticsindiamag.com/guide-to-zooming-slow-mo-one-stage-space-time-video-super-resolution/
Image Augmentation Pipelines with Tensorflow https://towardsai.net/p/machine-learning/building-complex-image-augmentation-pipelines-with-tensorflow-bed1914278d2
TensorFlow2.0-Examples https://github.com/YunYang1994/TensorFlow2.0-Examples
unadversarial https://github.com/microsoft/unadversarial/ https://analyticsindiamag.com/microsoft-research-unadversarial/
CNNs 'see' - FilterVisualizations, Heatmaps,Saliency Maps,saliency_map_guided,Heat Map Visualizations,GradCAM,Class Activation Maps,ZFNet,Lucid,Activation Atlas,Blur Integrated Gradients,concept whitening,Integrated Gradients,SmoothGrad,PytorchRevelio,Feature Visualizer, Guided Gradients, grad_cam,sensitivity_analysis,Captum,Preliminary Methods,Plot Model Architecture,Visualize Filters,Activation based Methods,Maximal Activation,Image Occlusion,Gradient based Methods,Gradient based Class Activation Map
Tools to Design or Visualize Architecture of Neural Network https://github.com/ashishpatel26/Tools-to-Design-or-Visualize-Architecture-of-Neural-Network
quiver Interactive convnet features visualization for Keras https://github.com/keplr-io/quiver
https://jair-neto.medium.com/a-powerful-method-for-explainability-of-object-detection-algorithms-ace0fe4623e7
https://github.com/utkuozbulak/pytorch-cnn-visualizations https://microscope.openai.com/models https://github.com/balavenkatesh3322/CV-pretrained-model
Mediapipe for Python https://google.github.io/mediapipe/
imageai.Detection for Object detection
cnn-raccoon interactive dashboards for your Convolutional Neural Networks with a single line of code https://github.com/lucko515/cnn-raccoon
deit https://github.com/facebookresearch/deit https://wandb.ai/thibault-neveu/detr-tensorflow-log/reports/Finetuning-DETR-Object-Detection-with-Transformers-on-Tensorflow-A-step-by-step-tutorial--VmlldzozOTYyNzQ https://github.com/Visual-Behavior/detr-tensorflow
awesome-computer-vision-models https://github.com/nerox8664/awesome-computer-vision-models
EfficientDet https://github.com/ravi02512/efficientdet-keras
Vision Transformer - Pytorch https://github.com/lucidrains/vit-pytorch https://github.com/alohays/awesome-visual-representation-learning-with-transformers
T2T-ViT https://analyticsindiamag.com/complete-guide-to-t2t-vit-training-vision-transformers-efficiently-with-minimal-data/ https://github.com/yitu-opensource/T2T-ViT
Explainability for Vision Transformers https://github.com/jacobgil/vit-explain
https://keras.io/examples/vision/image_classification_with_vision_transformer/
https://github.com/ashishpatel26/Vision-Transformer-Keras-Tensorflow-Pytorch-Examples https://github.com/google-research/vision_transformer
DeepLab-v3-plus Semantic Segmentation in TensorFlow https://github.com/rishizek/tensorflow-deeplab-v3-plus
DEEP LEARNING METHODS FOR 3D:3D human pose estimation= 2D pose estimation + matching,Integral Human Pose Regression,Towards 3D Human Pose Estimation in the
Wild: a Weakly-supervised Approach,A Simple Yet Effective Baseline for 3d Human Pose Estimation,
Data Augmentation apply to increase size of dataset and performance of model
low code object detection - detecto https://github.com/alankbi/detecto
AutoML https://github.com/dataloop-ai/AutoML
Object Detection with 10 lines of code-https://www.datasciencecentral.com/profiles/blogs/object-detection-with-10-lines-of-code https://towardsdatascience.com/object-detection-with-10-lines-of-code-d6cb4d86f606
Detecto https://github.com/alankbi/detecto https://medium.com/analytics-vidhya/computer-vision-in-healthcare-detection-of-fractures-3313fe6452fc
OneNet-https://analyticsindiamag.com/onenet/
Norfair https://github.com/tryolabs/norfair
Remo Improves Image Management https://www.freecodecamp.org/news/manage-computer-vision-datasets-in-python-with-remo/
yolo https://github.com/zzh8829/yolov3-tf2 https://github.com/ultralytics/yolov5 https://github.com/ashishpatel26/Yolov5-King-of-object-Detection https://github.com/sicara/tf2-yolov4
clip https://github.com/openai/CLIP
bayesian on CNN to reduce the overfitting and we can call CNN with applied Bayesian as a BayesianCNN https://analyticsindiamag.com/a-beginners-guide-to-bayesian-cnn/
3.Recurrent neural network (use when series of data)
1.RNN
2.GRU
3.LSTM (have memory cell,forget gate etc..)
Depth Gated RNNs,Peephole connection,Coupled Input and Forget,Clockwork RNNs,RNN Initialized Using Identity Matrix(IRNN)
𝗧𝗲𝗺𝗽𝗼𝗿𝗮𝗹 𝗖𝗼𝗻𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 better than LSTM/GRU https://github.com/ashishpatel26/tcn-keras-Examples
4.Information Discrimination Units (IDU) https://github.com/hjeun/idu
Train an LSTM Model ~30x Faster Using PyTorch with GPU https://towardsdatascience.com/how-to-train-an-lstm-model-30x-faster-using-pytorch-with-gpu-e6bcd3134c86
all above 3 models have bidirectional also based on problem statement use bidirectional models
Quasi-Recurrent Neural Network https://github.com/salesforce/pytorch-qrnn
textgenrnn https://github.com/minimaxir/textgenrnn
4.Generative adversarial network https://poloclub.github.io/ganlab/ https://developers.google.com/machine-learning/gan/training
gan lab https://poloclub.github.io/ganlab/
https://neptune.ai/blog/generative-adversarial-networks-gan-applications?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-generative-adversarial-networks-gan-applications
Diffusion Models Beat GANs on Image Synthesis https://paperswithcode.com/paper/diffusion-models-beat-gans-on-image-synthesis?from=n9
MUNIT: Multimodal Unsupervised Image-to-Image Translation (GAN)
https://jonathan-hui.medium.com/gan-gan-series-2d279f906e7b
generative adversarial transformers https://github.com/dorarad/gansformer
LipGAN https://github.com/Rudrabha/LipGAN Wav2Lip https://github.com/Rudrabha/Wav2Lip
BigGAN https://analyticsindiamag.com/hands-on-guide-to-biggan-with-python-code/
Cycle gan,Big GAN Style GAN,Dcgan,cGAN,SRGAN,InfoGAN,stargan,attan gan,stylegan,,PixelRNN,StackGAN,DiscoGAN,lsGAN,Conditional GAN(Pix2Pix),Progressive GANs( produces higher resolution images,Image-to-Image Translation),Face Inpainting,Super-resolution,Progressive Growing GAN,Instance-Conditioned GAN,Wasserstein GAN(improve image generation),ChromaGan,GANsformers,Conditional GAN and Unconditional GAN,Least Square GAN,Auxilary Classifier GAN,Dual Video Discriminator GAN,SRGAN,StackGAN,CycleGAN,WGAN
diffusion https://github.com/openai/guided-diffusion
https://www.analyticsvidhya.com/blog/2021/05/progressive-growing-gan-progan/
5 Alternatives To Deep Nostalgia https://analyticsindiamag.com/top-5-alternatives-to-deep-nostalgia/
MixNMatch https://github.com/Yuheng-Li/MixNMatch
Quantum GAN https://analyticsindiamag.com/now-gans-are-being-used-for-drug-discovery-complete-guide-to-quantum-gan-with-python-code/
https://analyticsindiamag.com/guide-to-differentiable-augmentation-for-data-efficient-gan-training/ https://analyticsindiamag.com/hands-on-python-guide-to-style-based-age-manipulation-sam-technique/
Imaginaire https://analyticsindiamag.com/guide-to-nvidia-imaginaire-gan-library-in-python/
Disentanglement https://analyticsindiamag.com/what-is-face-identity-disentanglement-and-how-it-outperformed-gans/
StyleFlow https://github.com/RameenAbdal/StyleFlow
https://github.com/hindupuravinash/the-gan-zoo https://analyticsindiamag.com/top-10-tools-for-generative-adversarial-networks/
5.Autoencoder
1.sparse Autoencoder
2.denoising Autoencoder
3.Contractive Autoencoder
4.stacked Autoencoder
5.deep Autoencoder
6.variational autoencoder
7.convolutional autoencoder
Beta Variational Autoencoder,VAE with Linear Normalizing Flows ,VAE with Inverse Autoregressive Flows ,Disentangled Beta Variational Autoencoder,Disentangling by Factorising (FactorVAE),Beta-TC-VAE (BetaTCVAE),Importance Weighted Autoencoder (IWAE),VAE with perceptual metric similarity,Wasserstein Autoencoder (WAE),Info Variational Autoencoder,VAMP Autoencoder (VAMP),Hyperspherical VAE (SVAE),Adversarial Autoencoder (Adversarial_AE),Variational Autoencoder GAN (VAEGAN) ,Vector Quantized VAE (VQVAE),Hamiltonian VAE (HVAE),Regularized AE with L2 decoder param (RAE_L2),Regularized AE with gradient penalty (RAE_GP),Riemannian Hamiltonian VAE (RHVAE)
https://github.com/zc8340311/RobustAutoencoder
Applications of AutoEncoders,Dimensionality reduction,Anomaly detection,Image denoising,Image compression,Image generation
6.BoltzmannMachines,Restricted Boltzmann Machine,deep belief network,deep BoltzmannMachines
7.Self Organizing Maps (SOM) , Fast Self-Organizing Map https://github.com/nmarincic/numbasom,minisom https://github.com/JustGlowing/minisom
8.Natural language processing
regex,PRegEx (https://github.com/manoss96/pregex)
Clean data(removing stopwords depond on problem ,lowering data,tokenization,postagging,stemmimg or lemmatization depond on problem,skipgram,n-gram,chunking)
clean text https://github.com/jfilter/clean-text
Cleaning and Pre-processing textual data with NeatText library Automated NLP Pre-Processing using Data-Purifier Library https://github.com/Elysian01/Data-Purifier
Nltk,spacy,genism,textblob,inltk,Indic NLP,StanfordNLP,Pattern,stanza,OpenNLP,polygot,corenlp,polyglot,PyDictionary,Huggiing face,spark nlp,allen nlp,rasa nlu,Megatron,texthero,Flair,textacy,finetune,gluon-nlp,VnCoreNLP,fasttext,Langid,PyCLD3,Guesslang,Parrot libraries
keyword library Rake_NLTK, Spacy, Textrank, Word cloud, KeyBert, Yake, MonkeyLearn API and Textrazor API.
jiant is an NLP toolkit https://github.com/nyu-mll/jiant
clean-text https://github.com/jfilter/clean-text https://www.youtube.com/watch?v=i2TjAgga1YU
indicnlp https://indicnlp.ai4bharat.org/samanantar/
Augmenting Data For NLP Tasks https://towardsdatascience.com/tips-tricks-augmenting-data-for-nlp-tasks-983e33ad55a7 https://amitness.com/2020/05/data-augmentation-for-nlp/ https://github.com/makcedward/nlpaug https://towardsdatascience.com/data-augmentation-in-nlp-2801a34dfc28
NLP Data Augmenting https://lnkd.in/eHa2cH6
Text Data Augmentation in Natural Language Processing with Texattack https://www.analyticsvidhya.com/blog/2022/02/text-data-augmentation-in-natural-language-processing-with-texattack/
Tagalog is our state-of-the-art solution for data management and labeling in Natural Language Processing https://www.tagalog.ai/tagalog/
https://github.com/jasonwei20/eda_nlp https://github.com/dsfsi/textaugment https://github.com/QData/TextAttack https://github.com/makcedward/nlpaug
nlp_profiler https://analyticsindiamag.com/complete-guide-on-nlp-profiler-python-tool-for-profiling-of-textual-dataset/
doccano text annotation tool https://github.com/doccano/doccano https://www.youtube.com/watch?v=vT-GE_jssPk https://github.com/doccano/auto-labeling-pipeline https://github.com/doccano/doccano-client https://doccano.herokuapp.com/
Data augmentation for NLP-https://github.com/makcedward/nlpaug
Data Augmentation library for text nlpaug https://towardsdatascience.com/data-augmentation-library-for-text-9661736b13ff
doccano,Parrot_Paraphraser,NLPAug,AugLy
detext-https://github.com/linkedin/detext
nlpaug-https://github.com/makcedward/nlpaug augmenty https://github.com/KennethEnevoldsen/augmenty
NLP-progress -https://github.com/sebastianruder/NLP-progress
Super Duper NLP Repo- https://notebooks.quantumstat.com/
Multilingual Representations for Indian Languages https://tfhub.dev/google/MuRIL/1
Natural Language Processing 365- https://ryanong.co.uk/natural-language-processing-365/
1 line for hundreds of NLP models and algorithms- https://github.com/JohnSnowLabs/nlu
simpletransformers
beautiful Wordclouds in Python https://towardsdatascience.com/how-to-easily-make-beautiful-wordclouds-in-python-55789102f6f5
Automate your Text Processing workflow in a single line of Python Code https://towardsdatascience.com/automate-your-text-processing-workflow-in-a-single-line-of-python-code-e276755e45de
quantumstat https://index.quantumstat.com/
Dynaboard: Moving beyond accuracy to holistic model evaluation in NLP https://ai.facebook.com/blog/dynaboard-moving-beyond-accuracy-to-holistic-model-evaluation-in-nlp/
gobbli for interactive NLP https://medium.com/rti-cds/using-gobbli-for-interactive-nlp-f60feb41e5cb
AutoReg Regex of string in Python https://github.com/SusmitPanda/AutoReg
Negation Handling Increasing Accuracy of Sentiment Classification
NLU,NLG,NER,text summarization,Sentiment Analysis,Text Classifications,machine translation,chat bot,Text Generation,Speech Recognition
Case Normalization,regex,Lowercasing,sent_tokenize,Tokenization,Remove Punctuations,Removing Stopwords,Removing Unicode,Removal of(Noise, URLs, Hashtag and User-mentions Hashtag),Replacing Emoticons,Removing Number,Correction of Spelling mistakes,Expanding Contractions,Removing Emojis,Convert Emoji,Remove Emoticon,Removing URLs,Hashtags,text normalization,Noise Removal,Punctuation,Spell Correction,Stemming or Lemmatization
1.One-hot-encoding,Index-based Encoding,Term Frequency,bag of words ,Bag of N-grams Model,Binary Term Frequency,(L1) Normalized Term Frequency,(L2) Normalized TF-IDF
2.Tfidf ,Weighted Class TF-IDF,tfidf + CHI²,HashingVectorizer
3.wordembedding : Use a pre-trained model , Self-Trained model
a.using pretrained model
i)word2vec( cbow,skipgram) ,AvgWord2vec
ii)glove https://medium.com/spark-nlp/1-line-to-glove-word-embeddings-with-nlu-in-python-baed152fff4d
iii)fast text
iv)MetaVec
b.creating own embedding (use when have huge data)
i)word2vec library
ii)keras embedding
elmo (store semantic of word)
Context-independent
Context-independent without machine learning Bag-of-words,TF-IDF
Context-independent with machine learning Word2vec (Bag of Words (CBoW) and Skip-Gram ) GloVe fastText
Context-dependent
Context-dependent and RNN based(elmo,cove)
Context-dependent and transformer-based (BERT ,xlm,RoBERTa,ALBERT)
contextual embeddings: AllenNLP ELMo, OpenAI’s GPT,GPT1,GPT2,GPT3, and Google’s BERT
Fast_Sentence_Embeddings Compute Sentence Embeddings Fast https://github.com/oborchers/Fast_Sentence_Embeddings
Universal Embeddings, Contextual Embeddings (Transformers),BERT Embeddings,Sentence Transformers,Sentence Vectors,Sentence Embedding
Transformer based embedding
3 b Tokenizer nlp(texs_to_sequences )
4.Document embedding-Doc2vec
5.sentence embedding
sense2vec,SENT2VEC,Universal sentence encoder,Sentence Transformers
Top2Vec
Topic Modelling https://towardsdatascience.com/april-edition-adventures-in-topic-modelling-7ee9081a48a0
Doc2Vec Distributed memory model , Distributed bag of word,Node2Vec,Top2Vec,Doc2Vec,Item2Vec
Elmo, BERT,Universal Sentence Encoder, Sentence Transformers
6.using rnn,lstm,gru
Conventional RNN,Deep Transition RNN,DT(S)-RNN,DOT-RNN,Stacked RNN
for above 3 models have bidirectional also
textgenrnn generate text https://github.com/minimaxir/textgenrnn
7.Encoder and Decoder(sequence to sequence), ProphetNet(new pretrained seq2seq model)
8.attention
self attention,Global Attention,Multi-Head Attention,Local Attention (monotonic,predictive),flash-attention,Fast and memory-efficient exact attention https://github.com/uzaymacar/attention-mechanisms
Seq2seq with Attention,Self-attentionm,Multi-head Attention
9.Transformer (big breakthrough in NLP) - http://jalammar.github.io/illustrated-transformer/
Build a Transformer in JAX from scratch https://theaisummer.com/jax-transformer/
Trankit is a Light-Weight Transformer-based Python Toolkit for Multilingual Natural Language Processing https://github.com/nlp-uoregon/trankit
FastFormers https://medium.com/ai-in-plain-english/fastformers-233x-faster-transformers-inference-on-cpu-4c0b7a720e1
Shrinking Transformers (reduce size) 1.quantization,distillation,pruning,
Reformer,Performers,vision transformer
Reformer: The Efficient Transformer
Longformer: The Long-Document Transformer
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
DeLighT: Deep and Light-weight Transformer https://analyticsindiamag.com/complete-guide-to-delight-deep-and-light-weight-transformer/
https://github.com/balavenkatesh3322/NLP-pretrained-model
Tree-Transformer https://github.com/yaushian/Tree-Transformer
Scalable Transformer-based Model https://analyticsindiamag.com/guide-to-perceiver-a-scalable-transformer-based-model/
Transformers Interpret https://towardsdatascience.com/introducing-transformers-interpret-explainable-ai-for-transformers-890a403a9470 https://github.com/cdpierse/transformers-interpret https://analyticsindiamag.com/hands-on-guide-to-the-evolved-transformer-on-neural-machine-translation/
Novel Interpretable Transformer https://github.com/hila-chefer/Transformer-Explainability https://analyticsindiamag.com/compute-relevancy-of-transformer-networks-via-novel-interpretable-transformer/
https://www.kdnuggets.com/2021/02/hugging-face-transformer-basics.html#.YE7gRy9s-LA.linkedin
mBART-50 https://www.youtube.com/watch?v=fxZtz0LPJLE&feature=youtu.be
Few-shot classification with SetFit and a custom dataset https://rubrix.readthedocs.io/en/docs-setfit_tutorial/tutorials/few-shot-classification-with-setfit.html
10.BERT,Packed BERT,BART,DynaBERT,SBERT,ConvBert,Quantized MobileBERT,ALBERT,ELECTRA,ARBERT,MARBERTElectra,Transformer-XL,Longformer,Reformer,DistilBERT,ELMo,ROBERTA,XLNet,XLM-RoBERTa,DeBERTa,T5,fastT5, CodeT5,mT5,ByT5,simpleT5,byt5,OnnxT5,FastT5,Linformer,DISTILBERT,GPT,GPT2,GPT3,gpt-neo,gpt-neox,GPT-J,aitextgen,PRADO,PET,BORT,MuRIL,Multitask Unified Model,aitextgen,AI21's 'Jurassic' language model,Turing NLG,Wu Dao 2.0,PanGu-Alpha,Gopher,Megatron model
https://neptune.ai/blog/bert-and-the-transformer-architecture-reshaping-the-ai-landscape
gpt3 https://www.producthunt.com/posts/100-resources-on-gpt-3
Graph4NLP https://dlg4nlp.github.io/index.html
Feedback Transformers from Facebook AI https://towardsdatascience.com/feedback-transformers-from-facebook-ai-221c5dd09e3f
DETR https://analyticsindiamag.com/how-to-detect-objects-with-detection-transformers/ https://github.com/dddzg/up-detr
DeiT https://analyticsindiamag.com/introducing-deit-data-efficient-image-transformers/ https://github.com/facebookresearch/deit
80+ NLP tasks https://medium.com/innerdoc/80-natural-language-processing-tasks-described-c777bc4974b3
Text-to-Image https://www.datasciencecentral.com/profiles/blogs/summarizing-popular-text-to-image-synthesis-methods-with-python
NLP: Pre-trained Sentiment Analysis https://medium.com/@b.terryjack/nlp-pre-trained-sentiment-analysis-1eb52a9d742c
Awesome-NLP-Resources -https://github.com/Robofied/Awesome-NLP-Resources https://shivanandroy.com/awesome-nlp-resources/ https://github.com/keon/awesome-nlp
10 Popular Keyword Extraction Algorithms in Natural Language Processing https://prakhar-mishra.medium.com/10-popular-keyword-extraction-algorithms-in-natural-language-processing-8975ada5750c
https://medium.com/@jatinmandav3/opinion-mining-sometimes-known-as-sentiment-analysis-or-emotion-ai-refers-to-the-use-of-natural-874f369194c0#:~:text=fastText%20is%20a%20library%20for,pretrained%20models%20for%20294%20languages
https://analyticsindiamag.com/top-ten-bert-alternatives-for-nlu-projects/ https://towardsdatascience.com/from-pre-trained-word-embeddings-to-pre-trained-language-models-focus-on-bert-343815627598
GPT2 generated Indian Food Recipes https://www.kaggle.com/nulldata/gpt2-generated-indian-food-recipes
http://jalammar.github.io/ http://jalammar.github.io/illustrated-bert/ http://jalammar.github.io/a-visual-guide-to-using-bert-for-the-first-time/
https://jalammar.github.io/explaining-transformers/ https://jalammar.github.io/hidden-states/
https://www.kdnuggets.com/2019/09/bert-roberta-distilbert-xlnet-one-use.html
11.Speech (Braina,Dragon Speech Recognition Solutions ,Winscribe,Gboard,Windows 10 Speech Recognition,Otter,Speechnotes,tts,OpenSpeech,FRILL,Vakyansh)
audio data augmentation https://github.com/iver56/audiomentations
speech to text
text to speech https://towardsdatascience.com/text-to-speech-one-small-step-by-mankind-to-create-lifelike-robots-54e19f843b21
Acoustic model,Speaker diarisation,apis,apiai,assemblyai,google-cloud-speech,pocketsphinx,SpeechRecognition,watson-developer-cloud,wit,Coqui TTS,Mozilla TTS, OpenTTS,ESPNet,PaddleSpeech,Wav2Vec, Whisper, DeepSpeech,Eesen,TensorFlowASR,Vosk,CMUSphinx,Pocketsphinx,KoNLPy,Madmom,HTK,Pysptk,Tortoise TTS,Bark,Musicgen,Riffusion
Microsoft IceCAPS is an Open Source Framework for Conversational Modeling https://pub.towardsai.net/microsoft-icecaps-is-an-open-source-framework-for-conversational-modeling-4f78492ca685
State-of-the-art Approaches to Building Open-Domain Conversational Agents https://www.topbots.com/conversational-ai-open-domain-chatbots/?utm_source=twitter&utm_medium=company_post&utm_campaign=conversational_open_domain_chatbots
LaMDA: our breakthrough conversation technology https://www.blog.google/technology/ai/lamda
assemblyai https://www.assemblyai.com/
bark https://github.com/suno-ai/bark
SpeechBrain A PyTorch Powered Speech Toolkit https://speechbrain.github.io/ https://github.com/speechbrain/speechbrain
Wav2vec-U learns to recognize #speech from unlabeled data https://venturebeat.com/2021/05/21/facebook-wav2vec-u-learns-to-recognize-speech-from-unlabeled-data/?utm_source=dlvr.it&utm_medium=linkedin
Wav2Vec2 https://huggingface.co/transformers/model_doc/wav2vec2.html https://www.youtube.com/watch?v=dJAoK5zK36M&feature=youtu.be
SincNet is a neural architecture for efficiently processing raw audio samples https://github.com/mravanelli/SincNet
HuggingFace Transformers ASR https://github.com/dennisbakhuis/Ecare_Brunch_ASR
English speech recognition https://github.com/openai/whisper
https://github.com/balavenkatesh3322/audio-pretrained-model
SpeechRecognition ASR2K: Speech Recognition https://github.com/xinjli/asr2k
audiomentations Python library for audio data augmentation https://github.com/iver56/audiomentations
googletrans (google Translator) https://pypi.org/project/googletrans/
lang-identification Google Compact Language Detector,FastText
𝗴𝗧𝗧𝗦 for text to speech conversion , 𝘀𝗽𝗲𝗲𝗰𝗵_𝗿𝗲𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝗼𝗻,TTS
Python/Pytorch app for easily synthesising human voices https://github.com/BenAAndrew/Voice-Cloning-App
Speech-Transformer-tf2.0 https://github.com/xingchensong/Speech-Transformer-tf2.0
The Super Duper NLP Repo https://notebooks.quantumstat.com/
ecco https://github.com/jalammar/ecco https://www.eccox.io/ https://www.youtube.com/watch?v=rHrItfNeuh0&feature=youtu.be
Language Interpretability Tool (LIT) is an open-source platform for visualization and understanding of NLP models https://pair-code.github.io/lit/
Language Interpretability Tool https://github.com/pair-code/lit https://ai.googleblog.com/2020/11/the-language-interpretability-tool-lit.html
autonlp https://analyticsindiamag.com/hands-on-guide-to-using-autonlp-for-automating-sentiment-analysis/
https://medium.com/towards-artificial-intelligence/natural-language-processing-nlp-with-python-tutorial-for-beginners-1f54e610a1a0
https://pakodas.substack.com/p/neural-search-on-indian-languages
https://www.linkedin.com/pulse/natural-language-processing-2020-year-review-ivan-bilan/?trackingId=CYfd1ZyLStu6x09tjVIoGw%3D%3D
ConvBert https://github.com/yitu-opensource/ConvBert
Python interface for building, loading, and using GloVe vectors https://github.com/Lguyogiro/pyglove
SentenceTransformers https://www.sbert.net/
Reformer – The Efficient Transformer https://analyticsindiamag.com/hands-on-guide-to-reformer-the-efficient-transformer/
Funnel-Transformer https://github.com/laiguokun/Funnel-Transformer
CLIP – Connecting Text To Images https://analyticsindiamag.com/hands-on-guide-to-openais-clip-connecting-text-to-images/
Topic Modeling in One Line with Top2Vec https://towardsdatascience.com/topic-modeling-in-one-line-with-top2vec-a413991aa0ef
MT5-https://venturebeat.com/2020/10/26/google-open-sources-mt5-a-multilingual-model-trained-on-over-101-languages/?utm_content=144321587&utm_medium=social&utm_source=linkedin&hss_channel=lcp-3740012
VADER does not require any training data https://pypi.org/project/vaderSentiment/ https://analyticsindiamag.com/sentiment-analysis-made-easy-using-vader/
APPLICATIONS OF MACHINE TRANSLATIO-Text-to-text,Text-to-speech,Speech-to-text,Speech-to-speech,Image (of words)-to-text
Google-GNMT (Tensorflow),Facebook-fairseq (Torch),Amazon-Sockeye (MXNet),NEMATUS (Theano),THUMT (Theano),OpenNMT (PyTorch),StanfordNMT (Matlab),DyNet-lamtram(CMU),EUREKA(MangoNMT
awesome-gpt3 https://github.com/elyase/awesome-gpt3
Robustness Gym: Evaluation Toolkit for NLP https://github.com/robustness-gym/robustness-gym
https://analyticsindiamag.com/best-nlp-based-seo-tools-for-2021/ https://towardsdatascience.com/5-nlp-models-that-you-need-to-know-about-754594a3225b
https://www.kdnuggets.com/2020/05/best-nlp-deep-learning-course-free.html https://analyticsindiamag.com/flair-hands-on-guide-to-robust-nlp-framework-built-upon-pytorch/
https://medium.com/modern-nlp/nlp-metablog-a-blog-of-blogs-693e3a8f1e0c
summarization https://github.com/hyunwoongko/summarizers ctrl-sum https://github.com/salesforce/ctrl-sum
classification,clustering,recommender systems,topic modelling,sentiment analysis,semantic analysis,summarization,machine translation,conversational interface,named entity recognition
F.Time Series Hands-On Guide To Atspy For Automating The Time-Series Forecasting https://github.com/Apress/hands-on-time-series-analylsis-python
here data split is different (train,test,validate)
here handling missing data different
Time Series Decomposition In Python trend, seasonality,Cyclical and noise https://towardsdatascience.com/time-series-decomposition-in-python-8acac385a5b2
Removing trend Differencing,Least square trends removal
Converting Non- stationary into stationary Detrending,Differencing,Transformation
Time Series Decomposition log,box-cox transformation,moving average
Removing seasonality Seasonal differencing,Seasonal means,Method of moving averages
generally used to impute data in Time Series
1.ffill
2.bfill
3.do mean of previous or future x samples and impute
4.take previous season value and impute (data with trend)
5.mean,mode,median,random sample imputation (data without trend and without seasonality)
6.linear interpolation(data with trend and without seasonality)
7.seasonal +interpolation(data with trend and with seasonality)
here model selection deponds on different property of data like stationary,trend,seasonality,cyclic
Anomaly Detection using Isolation Forest,AutoEncoders
Granger Causality Statistical Test use for variable usable for forecast
adfuller test for Stationarity Non Stationary Statistical Test - KPSS and ADF ACF, PACF, decomposition, ADF test
Handling Data with Regular Gaps using Facebook Prophet
models
1.AR,VR, VAR, MA, ARMA, ARIMA, auto arima(pmd arima) ,seasonal arima(SARIMA),SARIMAX models
2.Autoregressive,Vector Autoregression,Vector Autoregression Moving-Average,Vector Autoregression Moving-Average with Exogenous Regressors
3.Moving average,Exponential Moving average,Exponential Smoothing,Simple average, Holt’s linear trend method, Holt’s Winter seasonal method,DeepAR,N-BEATS
11 Classical Time Series Forecasting Methods in Python https://machinelearningmastery.com/time-series-forecasting-methods-in-python-cheat-sheet/
4.XGBoost,Lstm(neural network),DeepAR ( An RNN Algorithm)
5.GARCH
atspy Automated time-series models
6.Navie forecasts
7.Smoothing (moving average,exponential smoothing)
8.Facebook prophet (note:expceted date column as ds and target column as y) https://thecleverprogrammer.com/2020/12/14/facebook-prophet-model-with-python/
NeuralProphet Model- https://ourownstory.github.io/neural_prophet/model-overview/ https://thecleverprogrammer.com/2021/01/28/neuralprophet-model-with-python/
bulbea Deep Learning based Python Library for Stock Market Prediction and Modelling https://github.com/achillesrasquinha/bulbea
PyTorch Forecasting enables deep learning models for time-series forecasting pytorch-ts https://github.com/zalandoresearch/pytorch-ts
ETSformer-pytorch https://github.com/lucidrains/ETSformer-pytorch
Transformer Networks to build a Forecasting model https://towardsdatascience.com/how-to-use-transformer-networks-to-build-a-forecasting-model-297f9270e630
Temporal Fusion Transformer (By Google)
hmmlearn https://github.com/ushareng/StockPricePredictionUsingHMM_Byte/blob/master/StockPricePredictionUsingHMM.ipynb
pyramid-arima https://github.com/tgsmith61591/pyramid
pyflux: time series library: https://github.com/RJT1990/pyflux
orbit https://eng.uber.com/orbit/
greykite A flexible, intuitive and fast forecasting library https://github.com/linkedin/greykite https://www.analyticsvidhya.com/blog/2021/05/greykite-time-series-forecasting-in-python/
Silverkite
LinkedIn open-sources Greykite, a library for time series forecasting https://github.com/linkedin/greykite/stargazers
stumpy https://github.com/TDAmeritrade/stumpy
Giotto-Time Time-Series Forecasting Python Library https://github.com/giotto-ai/giotto-time https://analyticsindiamag.com/guide-to-giotto-time-a-time-series-forecasting-python-library/
Informer (for Long Sequence Time-Series Forecasting) https://analyticsindiamag.com/informer/
tfcausalimpact https://github.com/WillianFuks/tfcausalimpact
deepar is global model https://www.youtube.com/watch?v=xcbj0RE3kfI&list=PL3N9eeOlCrP5cK0QRQxeJd6GrQvhAtpBK&index=14
pmdarima for Auto ARIMA
GluonTS https://github.com/awslabs/gluon-ts
sktime — a unified time-series framework for Scikit-Learn
tsfresh — a magical library for feature extraction in time-series datasets
ThymeBoost Forecasting with Gradient Boosted Time Series Decomposition https://github.com/tblume1992/ThymeBoost
darts A python library for easy manipulation and forecasting of time series https://github.com/unit8co/darts
Kats https://github.com/facebookresearch/Kats
Time Series Outlier Detection with ThymeBoost
AtsPy: Automated Time Series Models in Python https://github.com/firmai/atspy
Merlion: A Machine Learning Framework for Time Series Intelligence https://github.com/salesforce/Merlion
stumpy powerful and scalable Python library for modern time series analysis https://github.com/TDAmeritrade/stumpy
mlforecast Scalable machine learning based time series forecasting https://github.com/Nixtla/mlforecast
statsforecast Lightning ⚡️ fast forecasting with statistical and econometric models https://github.com/Nixtla/statsforecast
9.Holts winter,Holts linear trend
10.Auto_Timeseries by auto-ts https://www.youtube.com/watch?v=URUiVD37fns&list=PL3N9eeOlCrP5cK0QRQxeJd6GrQvhAtpBK&index=24 tell best model for data
AutoTS-https://analyticsindiamag.com/hands-on-guide-to-autots-effective-model-selection-for-multiple-time-series/ https://github.com/AutoViML/Auto_TS
Automated Time Series Forecasting https://github.com/winedarksea/AutoTS , No-Code AI Forecasting Platform https://datafloat.ai/
AutoML for time series: advanced approaches with FEDOT framework https://towardsdatascience.com/automl-for-time-series-advanced-approaches-with-fedot-framework-4f9d8ea3382c
AutoML for time series: definitely a good idea https://towardsdatascience.com/automl-for-time-series-definitely-a-good-idea-c51d39b2b3f
AutoReg Regex of string in Python https://github.com/SusmitPanda/AutoReg
pytsal low-code open-source python framework for Time Series analysis,visualization,forecasting along with AutoTS https://github.com/KrishnanSG/pytsal
Automated Time Series Forecasting https://github.com/winedarksea/AutoTS
Forecasting with H2O AutoML https://github.com/business-science/modeltime.h2o/
Forecasting Stock Prices Using Stocker https://medium.com/mlearning-ai/forecasting-stock-prices-using-stocker-7d2ac15966f5
MiniRocket: Fast(er) and Accurate Time Series Classification https://towardsdatascience.com/minirocket-fast-er-and-accurate-time-series-classification-cdacca2dcbfa
modeltime https://github.com/business-science/modeltime
GluonTS , PytorchTS https://analyticsindiamag.com/gluonts-pytorchts-for-time-series-forecasting/
stocker https://medium.datadriveninvestor.com/forecasting-stock-prices-using-stocker-66503c26307a
11.Temporal Convolutional Neural
12.Atspy For Automating The Time-Series Forecasting-https://analyticsindiamag.com/hands-on-guide-to-atspy-for-automating-the-time-series-forecasting/
13.Darts-https://analyticsindiamag.com/hands-on-guide-to-darts-a-python-tool-for-time-series-forecasting/
14.Bayesian Neural Network , TsEuler
15.PyFlux (easy way to compare different models)-https://analyticsindiamag.com/pyflux-guide-python-library-for-time-series-analysis-and-prediction/
16.Orbit , DeepAR ,NeuralProphet(https://github.com/ourownstory/neural_prophet https://ourownstory.github.io/neural_prophet/model-overview/)
IBM’s AutoAI automates time series forecasting https://www.ibm.com/blogs/research/2021/03/autoai-time-series/?utm_campaign=Learning%20Posts&utm_content=159454790&utm_medium=social&utm_source=twitter&hss_channel=tw-3018841323
Kats all in 1 time seres data https://github.com/facebookresearch/kats https://facebookresearch.github.io/Kats/
orbit https://analyticsindiamag.com/hands-on-guide-to-orbit-ubers-python-framework-for-bayesian-forecasting-inference/ https://github.com/uber/orbit
best article-https://www.analyticsvidhya.com/blog/2018/02/time-series-forecasting-methods/
TimeSynth https://github.com/TimeSynth/TimeSynth https://analyticsindiamag.com/guide-to-timesynth-a-python-library-for-synthetic-time-series-generation/
time series visualization tool https://plotjuggler.io/
Time Series Anomaly Detection using Generative Adversarial Networks(TadGAN) https://analyticsindiamag.com/hands-on-guide-to-tadgan-with-python-codes/
fastquant — Backtest and optimize your trading strategies with only 3 lines of code https://github.com/enzoampil/fastquant
pytorch-forecasting https://github.com/jdb78/pytorch-forecasting https://analyticsindiamag.com/guide-to-pytorch-time-series-forecasting/
https://pytorch-forecasting.readthedocs.io/en/latest/ https://pytorch-forecasting.readthedocs.io/en/latest/tutorials/ar.html
Complex Exponential Smoothing (CES) which can handle both stationary and non-stationary processes and model a wide spectum of level and trend time-series. https://github.com/Nixtla/statsforecast/tree/main/experiments/ces
sktime-https://github.com/alan-turing-institute/sktime https://analyticsindiamag.com/sktime-library/
autocast https://github.com/andyzoujm/autocast
tsfresh – a magical library for feature extraction in time-series datasets.
atspy https://github.com/firmai/atspy
tcn https://towardsdatascience.com/farewell-rnns-welcome-tcns-dd76674707c8
Pastas https://analyticsindiamag.com/guide-to-pastas-a-python-framework-for-hydrogeological-time-series-analysis/ https://github.com/pastas/pastas
stockDL https://github.com/ashishpapanai/stockDL
decompsition https://towardsdatascience.com/time-series-decomposition-in-python-8acac385a5b2
Bayesian Diffusion Modeling https://www.topbots.com/bayesian-diffusion-modeling/
Top 10 Python Tools For Time Series Analysis https://analyticsindiamag.com/top-10-python-tools-for-time-series-analysis/
fine Tune Your Machine Learning Models To Improve Forecasting Accuracy https://www.kdnuggets.com/2019/01/fine-tune-machine-learning-models-forecasting.html
add extra features https://towardsdatascience.com/the-demand-sales-forecast-technique-every-data-scientist-should-be-using-to-reduce-error-1c6f25add9cb
https://machinelearningmastery.com/time-series-forecasting-methods-in-python-cheat-sheet/
https://www.machinelearningplus.com/time-series/time-series-analysis-python/ https://www.datasciencecentral.com/profiles/blogs/list-of-time-series-methods-in-one-picture
https://github.com/Apress/hands-on-time-series-analylsis-python
https://otexts.com/fpp2/simple-methods.html
https://analyticsindiamag.com/top-time-series-deep-learning-methods/
book https://otexts.com/fpp2/
deep_autoviml Build tensorflow keras model pipelines in a single line of code https://github.com/AutoViML/deep_autoviml
G.𝐆𝐫𝐚𝐩𝐡 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬
Spatial-temporal graph neural networks,Structural Deep Network Embedding,Convolutional Graph Neural Network,GraphSAGE,Graph convolutional recurrent network,Diffusion convolutional recurrent neural network,Graph LSTM,Graph Autoencoders,Variational Graph Auto-Encoders,Graph Attention Networks
G.Semi supervised learning,Self-Supervised Learning,Multi-Instance Learning
self-training meta-estimator for semi-supervised learning
skweak: A Python Toolkit For Applying Weak Supervision To NLP Tasks https://analyticsindiamag.com/meet-skweak-a-python-toolkit-for-applying-weak-supervision-to-nlp-tasks/
10 Self-Supervised Learning Frameworks & Libraries To Use In 2021 analyticsindiamag.com/10-self-supervised-learning-frameworks-libraries-to-use-in-2021/
Self-Supervised Learning https://github.com/jason718/awesome-self-supervised-learning
OpenMMLab Self-Supervised Learning https://github.com/open-mmlab/mmselfsup
awesome-self-supervised-learning https://github.com/jason718/awesome-self-supervised-learning
Self-supervised Video Object Segmentation https://charigyang.github.io/motiongroup/
lightly A python library for self-supervised learning on images https://github.com/lightly-ai/lightly
Weak Supervision: The Art Of Training ML Models From Noisy Data https://analyticsindiamag.com/weak-supervision-the-art-of-training-ml-models-from-noisy-data/
snorkel and skweak, are there other libraries to explore for weak supervision in NLP
8 Resources To Learn Self-Supervised Learning In 2021 https://analyticsindiamag.com/top-8-resources-to-learn-self-supervised-learning-in-2021/
Barlow Twins: Self-Supervised Learning via Redundancy Reduction https://analyticsindiamag.com/a-guide-to-barlow-twins-self-supervised-learning-via-redundancy-reduction/ https://github.com/facebookresearch/barlowtwins
skweak: A Python Toolkit For Applying Weak Supervision To NLP Tasks https://analyticsindiamag.com/meet-skweak-a-python-toolkit-for-applying-weak-supervision-to-nlp-tasks/
H.Active learning,Multi-Task Learning,Online Learning
Active Learning Frameworks https://towardsdatascience.com/a-summary-of-active-learning-frameworks-3165159baae9
Meta Learning https://github.com/sudharsan13296/Awesome-Meta-Learning
Avalanche: A Python Library for Continual Learning https://analyticsindiamag.com/avalanche-a-python-library-for-continual-learning/
Reptile (OpenAI’s Latest Meta-Learning Algorithm) https://github.com/openai/supervised-reptile https://analyticsindiamag.com/reptile-openais-latest-meta-learning-algorithm/
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks" https://github.com/cbfinn/maml
I.Transfer learning(Inductive Transfer learning(similar domain,different task),Unsupervised Transfer Learning(different task,different domain but similar enough) ,Transductive Transfer Learning(similar task,different domain)),Inductive transfer learning(labeled data is the same for the target and source domain but the tasks the model works on are different),Unsupervised transfer learning(unsupervised tasks for both source and target tasks),self taught learning,Homogeneous Transfer Learning,Heterogenous Transfer Learning
Transfer Learning Using TensorFlow Keras https://analyticsindiamag.com/transfer-learning-using-tensorflow-keras/
https://github.com/artix41/awesome-transfer-learning
J.Deep dream,Style transfer
K.One-shot learning,Zero-shot learning
l.Incremental Training https://blog.rasa.com/rasa-new-incremental-training/
https://github.com/ChristosChristofidis/awesome-deep-learning
101 Machine Learning Algorithms for Data Science with Cheat Sheets https://blog-datasciencedojo-com.cdn.ampproject.org/c/s/blog.datasciencedojo.com/machine-learning-algorithms/amp/
TYPES OF ACTIVATION FUNCTIONS: LINEAR ACTIVATION,RELU,LEAKY RELU,GELU,Parameterized ReLU,Shifted ReLU, Noisy ReLU,SIGMOID ACTIVATION,TANH ACTIVATION,elu,PReLU,Modifying ReLU,Shifted ReLU,Softmax,Swish,Softplus,Mish,Smooth reLU,GELU,Swish,Elliot
Optimizer- Gradient Descent(Batch Gradient Descent,Stochastic Gradient Descent,Mini batch Gradient Descent),sgd with momentum,Adagrad,RMSProp,AMSGrad,Adam,AdaBelief,MADGRAD,Nero,
https://analyticsindiamag.com/ultimate-guide-to-pytorch-optimizers/ https://analyticsindiamag.com/guide-to-tensorflow-keras-optimizers/
Regularization- L1, L2,elasticnet, dropout, early stopping, and data augmentation,batch normalisation,Layer Normalization,Group Normalization,tree purning,DropBlock,DropConnect,Learning rate schedulingWeight Decay,Gradient clipping,Adaptive optimizer
Addressing Overfitting - 13 Methods
- Dimensionality Reduction
- Feature Selection
- Early Stopping
- K-Fold Cross-Validation
- Creating Ensembles
- Pre‐Pruning
- Post‐Pruning
- Noise Regularization
- Dropout Regularization
- L1 and L2 Regularization
- Data (Image) Augmentation
- Adding More Training Data
- Reducing Network Width & Depth
DropBlock: A New Regularization Technique https://pub.towardsai.net/dropblock-a-new-regularization-technique-e926bbc74adb
Learning rate scheduling (Learning rate finder),Weight Decay,Gradient clipping,Cyclic Learning Rate
weight initialization Normal Distribution,initialized to the same value,Xavier Initialization,He Norm Initialization,
Different Normalization Layers - https://towardsdatascience.com/different-normalization-layers-in-deep-learning-1a7214ff71d6
Hyperparameters Number of hidden layers,Dropout,activation function,Weights initialization , learning rate,epoch, iterations and batch size
DropBlock-Keras-Implementation https://github.com/iantimmis/DropBlock-Keras-Implementation https://github.com/miguelvr/dropblock https://github.com/DHZS/tf-dropblock
standard dropout,early dropout,late dropout
Hyperparameter tuning
https://analyticsindiamag.com/top-8-approaches-for-tuning-hyperparameters-of-machine-learning-models/ https://analyticsindiamag.com/top-10-open-source-hyperparameter-optimisation-libraries-for-ml-models/
https://github.com/balavenkatesh3322/hyperparameter_tuning
A.manual search
a.GridSearchCV (check every given parameter so take long time),TuneGridSearchCV
HalvingGridSearch https://towardsdatascience.com/11-times-faster-hyperparameter-tuning-with-halvinggridsearch-232ed0160155 https://towardsdatascience.com/faster-hyperparameter-tuning-with-scikit-learn-71aa76d06f12
tune-sklearn https://github.com/ray-project/tune-sklearn (TuneGridSearchCV)
b.RandomizedSearchCV (search randomly narrow down our time) with Scikit-learn, Scikit-Optimize,Hyperopt,TuneSearchCV
HalvingRandomSearchCV
c.Optuna,Hyperopt,Scikit-optimize,Keras Tuner,Ray-tune,Bayesian Optimization,Bayesian Optimization with Gaussian Processes (BO-GP),Bayesian Optimization with Tree-structured Parzen Estimator (BO-TPE),Particle swarm optimization (PSO),Genetic algorithm (GA)Hyperopt,bayes search,Hyperband and BOHB,HyperOpt-Sklearn,Bayes Search,Scikit Optimize,TPE,Multivariate TPE,HyperBand,Bayesian Optimization,exhaustive search, heuristic search,multi-fidelity optimization,NNI,DEAP,OptFormer,hgboost,Hyperopt,Sklearn-genetic,GPyOpt,pyGPGO,Mango,mlmachine,Polyaxon,BayesianOptimization,Talos,SHERPA,Scikit-Optimize,GPyOpt,SMAC, Simulated annealing (SA),Genetic algorithms (GAs),Particle swarm optimization (PSO),Population-Based Training (PBT),Multi-Fidelity Optimization,DEAP,SMAC,Ray Tune,Google’s Vizer, Microsoft’s NNI,Keras tuner,BayesianOptimization,GPyOpt,SigOpt
Bayesian Optimization: https://github.com/fmfn/BayesianOptimization
Scikit Optimize: https://github.com/scikit-optimize/scikit-optimize
Pyro: https://github.com/pyro-ppl/pyro
BoTorch: https://github.com/pytorch/botorch
RBFOpt library for black-box optimization https://github.com/coin-or/rbfopt
Bayesian search with Gaussian processes,bayesian search with Random Forests,Bayesian search with GBMs
Bayesian Optimization Using BoTorch https://analyticsindiamag.com/guide-to-bayesian-optimization-using-botorch/
hyperparameter optimization https://github.com/LiYangHart/Hyperparameter-Optimization-of-Machine-Learning-Algorithms
Hyperopt hyperas https://www.kdnuggets.com/2018/12/keras-hyperparameter-tuning-google-colab-hyperas.html
hyperopt http://hyperopt.github.io/hyperopt/
hypertune-using-scikit-optimize BayesSearchCV
HpBandSter https://github.com/automl/HpBandSter hpsklearn https://medium.com/mlearning-ai/automatic-hyperparameter-optimization-6a1692c2ebee
hypopt https://github.com/cgnorthcutt/hypopt https://medium.com/mlearning-ai/automatic-hyperparameter-optimization-6a1692c2ebee
HiPlot https://analyticsindiamag.com/this-new-tool-helps-developers-in-effective-hyperparameter-tuning/
botorch Bayesian optimization https://github.com/pytorch/botorch
OCTIS https://github.com/mind-lab/octis
hyperband https://neptune.ai/blog/hyperband-and-bohb-understanding-state-of-the-art-hyperparameter-optimization-algorithms
Spearmint https://github.com/JasperSnoek/spearmint/
tuun Hyperparameter tuning via uncertainty modeling https://github.com/petuum/tuun
tune-sklearn https://github.com/ray-project/tune-sklearn/
NeuPy http://neupy.com/2016/12/17/hyperparameter_optimization_for_neural_networks.html#id24
Vizier
ConfigSpace https://automl.github.io/ConfigSpace/master/ https://towardsdatascience.com/tuning-xgboost-with-xgboost-writing-your-own-hyper-parameters-optimization-engine-a593498b5fba
NatureInspiredSearchCV https://github.com/timzatko/Sklearn-Nature-Inspired-Algorithms
d.Sequential Model Based Optimization(Tuning a scikit-learn estimator with skopt)
e.Optuna https://analyticsindiamag.com/hands-on-python-guide-to-optuna-a-new-hyperparameter-optimization-tool/
f.Genetic Algorithms,Gradient-based optimization
darwin-mendel Genetic Algorithm for Hyper-Parameter Tuning https://manishagrawal-datascience.medium.com/genetic-algorithm-for-hyper-parameter-tuning-1ca29b201c08
g.Keras tuner (Random Search Keras Tuner,HyperBand Keras Tuner,Bayesian Optimization Keras Tuner,Hyperas ) https://sukanyabag.medium.com/automated-hyperparameter-tuning-with-keras-tuner-and-tensorflow-2-0-31ec83f08a62
Keras Hyperparameter Tuning with aisaratuners Library https://aisaradeepwadi.medium.com/advance-keras-hyperparameter-tuning-with-aisaratuners-library-78c488ab4d6a
hyperas Automating Hyperparameter Tuning of Keras Model https://github.com/maxpumperla/hyperas
storm-tuner https://github.com/ben-arnao/StoRM https://medium.com/geekculture/finding-best-hyper-parameters-for-deep-learning-model-4df7a17546c2
Hyperas https://towardsdatascience.com/automating-hyperparameter-tuning-of-keras-model-4fe69b8dedee
hyperopt-sklearn https://github.com/hyperopt/hyperopt-sklearn
Deep AutoViML https://github.com/AutoViML/deep_autoviml
h.Scikit-Optimize,Optuna,Hyperopt,Multi-fidelity Optimization,Gradient-based optimization,Evolutionary optimization,Population-based,Bayes Search
Scikit-Optimize library comes with BayesSearchCV implementation
mle-hyperopt Lightweight Hyperparameter Optimization Tool https://github.com/mle-infrastructure/mle-hyperopt
h.Hyperparameter Optimization https://github.com/awslabs/syne-tune
i.ray[tune] and aisaratuners https://towardsdatascience.com/choosing-a-hyperparameter-tuning-library-ray-tune-or-aisaratuners-b707b175c1d7
raytune https://docs.ray.io/en/master/tune/index.html https://docs.ray.io/en/latest/tune/index.html
k.model_search https://github.com/google/model_search https://analyticsindiamag.com/hands-on-guide-to-model-search-a-tensorflow-based-framework-for-automl/
Optimize machine learning models https://www.tensorflow.org/model_optimization
Milano https://github.com/NVIDIA/Milano
Tree-structured Parzen Estimators - TPE , TPE with Hyperopt
Hyperparameter Tuning with the HParams Dashboard
baytune https://www.kdnuggets.com/2021/03/automating-machine-learning-model-optimization.html
Dragonfly https://analyticsindiamag.com/guide-to-scalable-and-robust-bayesian-optimization-with-dragonfly/
Pywedge https://www.analyticsvidhya.com/blog/2021/02/interactive-widget-based-hyperparameter-tuning-and-tracking-in-pywedge/
CapsNet Hyperparameter Tuning with Keras https://towardsdatascience.com/scikeras-tutorial-a-multi-input-multi-output-wrapper-for-capsnet-hyperparameter-tuning-with-keras-3127690f7f28
GPyTorch: A Python Library For Gaussian Process Models https://analyticsindiamag.com/guide-to-gpytorch-a-python-library-for-gaussian-process-models/
Auto-PyTorch https://github.com/automl/Auto-PyTorch
l.SMAC https://www.automl.org/automated-algorithm-design/algorithm-configuration/smac/ https://towardsdatascience.com/automl-for-fast-hyperparameters-tuning-with-smac-4d70b1399ce6
m.faster Hyper Parameter Tuning(sklearn-nature-inspired-algorithms) https://pypi.org/project/sklearn-nature-inspired-algorithms/
n.talos Neural network and hyperparameter optimization using Talos https://www.analyticsvidhya.com/blog/2021/05/neural-network-and-hyperparameter-optimization-using-talos/
https://towardsdatascience.com/10-hyperparameter-optimization-frameworks-8bc87bc8b7e3
https://mlwhiz.com/blog/2020/02/22/hyperspark/?utm_campaign=100x-faster-hyperparameter-search-framework-with-pyspark&utm_medium=social_link&utm_source=missinglettr
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective https://github.com/microsoft/DeepSpeed
o.shap-hypetune https://github.com/cerlymarco/shap-hypetune https://towardsdatascience.com/shap-for-feature-selection-and-hyperparameter-tuning-a330ec0ea104
mlmachine,Polyaxon,BayesianOptimization,Talos,SHERPA,Scikit-Optimize,GPyOpt
p.Hyperactive https://github.com/SimonBlanke/Hyperactive
Hyperopt, Optuna, and Ray,SCIKIT-OPTIMIZE,SMAC,Multi-fidelity Optimization,Successive Halving,Hyperband BOHB,SMBOSearch
OMLT optimization https://github.com/cog-imperial/OMLT
HyperOpt http://hyperopt.github.io/hyperopt/ Optuna https://optuna.org/ Scikit-optimize https://scikit-optimize.github.io/stable/ SigOpt https://sigopt.com/
DeepHyper Hyperparameter Search for Deep Neural Networks https://github.com/deephyper/deephyper
lipo hyperparameter tuning https://github.com/jdb78/lipo
Weights and Biases to Perform Hyperparameter Optimization https://hackernoon.com/using-weights-and-biases-to-perform-hyperparameter-optimization
Cross validation techniques- https://towardsdatascience.com/understanding-8-types-of-cross-validation-80c935a4976d
a.Exhaustive, where the method learn and test on every single possibility of dividing the dataset into training and testing subsets. b.Non-exhaustive cross validation methods where all ways of splitting the sample are not computed.
1.Loocv
2.Kfoldcv,Repeated K-Folds Method,Shuffle & Split cross-validation
3.Stratfied cross validation,Stratified K-fold CV,Group K-fold,StratifiedGroupKFold,StratifiedShuffleSplit,Nested K-folds,Random split KFold,Walk forward,Group Time Series,Purged Group KFold,Combinatorial Purged Group KFold
4.Repeated K-folds,RepeatedStratifiedKFold,Repeated random subsampling CV
5.Holdout cross-validation
6.Repeated cross-validation,Repeated K-folds,Blocked Cross-Validation Method, Nested Cross-Validation Method,Group Cross-Validation,GroupShuffleSplit,Blocked Cross-Validation
7.LeaveOneOut,Leave P out ,Leave-one-out cross-validation,Leave-One-Group-Out Method,Leave-P-Group-Out Method
8.Time Series cross-validation,Time Series Split cross-validation ,Rolling Cross-Validation,Rolling Time Series Cross Validation,Rolling Window Cross-Validation,Monte Carlo Cross-Validation,Holdout Time Series Cross-Validation,Time Series Cross-Validation with a Gap,Sliding Time Series Cross-Validation,GapKFold,GapLeavePOut,GapRollForward
9.ShuffleSplit Cross Validation,Group Shuffle Split,Simple Time Split Validation,Sliding Window Validation,Expanding Window Validation
10.Group KFold Cross Validation
11.Monte-Carlo Cross Validation,Blocked cross-validation,Blocked K-Fold Cross-Validation,Modified K-Fold Cross-Validation
Tensorboard,Neptune,TensorFlow Profiler to visualization of model performance
Distributed Training with TensorFlow
6.Testing model
Text Robustness Evaluation Platform https://github.com/textflint/textflint
Generally used metrics
Always check bias variance tradeoff to know how model is performing
Locust Performance Testing ML Serving APIs With Locust https://www.analyticsvidhya.com/blog/2021/06/performance-testing-ml-serving-apis-with-locust/
Model can be overfitting(low bias,high variance),underfitting(high bias,high variance),good fit(low bias,low variance)
https://scikit-learn.org/stable/modules/model_evaluation.html https://scikit-learn.org/stable/modules/classes.html#module-sklearn.linear_model
https://stanford.edu/~shervine/teaching/cs-229/cheatsheet-machine-learning-tips-and-tricks
KS test to evaluate the separation between class distribution
Evaluating the potential return of a model with Lift, Gain, and Decile Analysis
1.Regression task - mean-squared-error, Root-Mean-Squared-Error,mean-absolute error, R², Adjusted R²,Cross-entropy loss,Mean percentage error
2.Classification task-Accuracy,confusion matrix,Precision,Recall,F1 Score,Binary Crossentropy,Categorical Crossentropy,AUC-ROC curve,AUPRC,log loss,Average precision,Mean average precision
3.Reinforcement learning - generally use rewards
4.Incase of machine translation use bleu score
5.Clustering then use External: Adjusted Rand index, Jaccard Score, Purity Score,Rand Index,Mutual Information,V-measure,Fowlkes-Mallows Scores,DBCV
Internal:silhouette_score, Davies-Bouldin Index, Dunn Index
autoelbow,elbow,Davies-Bouldin Index,Calinski-Harabasz Index
https://towardsdatascience.com/performance-metrics-in-machine-learning-part-3-clustering-d69550662dc6
6.Object Detection loss-localization loss,classification loss,Focal Loss,IOU,L2 loss
7.Distance Metrics - Euclidean Distance,Manhattan Distance,Minkowski Distance,Hamming Distance https://towardsdatascience.com/9-distance-measures-in-data-science-918109d069fa
Dimensionality Reduction Metrics - Cumulative Explained Variance,Trustworthiness,Sammon’s Mapping
8.Recommender Systems https://parthchokhra.medium.com/evaluating-recommender-systems-590a7b87afa5
Accuracy Metrics (RMSE, MAE),Top-N Hit Rate
RecList: The better way to evaluate recommender systems
Similarity metrics : Cosine similarity,Jaccard similarity,Euclidean distance Predictive metrics: MAE,RMSE
metric-Built-in metrics, Custom metric without external parameters,Custom metric with external parameters,Subclassing custom metric layer
Robustness Gym: Evaluation Toolkit for NLP https://github.com/robustness-gym/robustness-gym
https://medium.com/swlh/custom-loss-and-custom-metrics-using-keras-sequential-model-api-d5bcd3a4ff28
loss-Built-in loss, Custom loss without external parameters,Custom loss with external parameters,Subclassing loss layer
https://analyticsindiamag.com/all-pytorch-loss-function/ https://analyticsindiamag.com/ultimate-guide-to-loss-functions-in-tensorflow-keras-api-with-python-implementation/
tensorwatch Debugging, monitoring and visualization for Python Machine Learning and Data Science https://github.com/microsoft/tensorwatch
Types of Data Drift : Concept drift,Virtual drift,Covariate shift,Prior probability shift,Annotator drift,Data poisoning
mitigate the effects of data drift: Regular retraining,Data preprocessing,Data augmentation,Monitoring,Online learning,Domain adaptation,Annotator and data quality control
Methods to Detect Drift A) Statistical Approaches,Page-Hinkley method,Kolmogorov-Smirnov Test,Population Stability Index (PSI),Kullback-Leibler (KL) divergence,Jensen-Shannon divergence, Wasserstein Distance B) Model-Based Approach C) Adaptive Sliding Window d)Data visualization tools e)Model performance monitoring f)Drift detection libraries
𝐭𝐨𝐨𝐥𝐬 𝐭𝐨 𝐝𝐞𝐭𝐞𝐜𝐭 𝐦𝐨𝐝𝐞𝐥 𝐝𝐫𝐢𝐟𝐭𝐬 : 𝐰𝐡𝐲𝐥𝐨𝐠𝐬,𝐄𝐯𝐢𝐝𝐞𝐧𝐭𝐥𝐲,𝐀𝐥𝐢𝐛𝐢 𝐃𝐞𝐭𝐞𝐜𝐭
Steps to take when there is an occurrence of drift Check Data Quality, Investigate,Retrain the model,Rebuild the model, Pause the model and Fallback
Ways to handle Drift in Production a) Rapidly adapt to concept drift b) Be resistant to noise while distinguishing it from concept drift c) Notice and handle severe drift in model performance.
article link https://medium.com/@dummahajan/combating-data-drift-the-fight-for-model-accuracy-2c619ee1e33a
Docker and Kubernetes
simplest way to serve your ML models on Kubernetes https://towardsdatascience.com/the-simplest-way-to-serve-your-ml-models-on-kubernetes-5323a380bf9f
7.deployment https://github.com/piyushpathak03/Model-Deployment
Train: one off, batch and real-time/online training
Serve: Batch, Realtime (Database Trigger, Pub/Sub, web-service, inApp)
Continuously Monitor the Behaviour of Deployed Models https://se-ml.github.io/best_practices/04-monitor_models_prod/
Model Monitoring https://www.kdnuggets.com/2021/03/machine-learning-model-monitoring-checklist.html
Automate Model Deployment https://se-ml.github.io/best_practices/04-auto_model_packaging/
Platform as a Service (PaaS),Infrastructure as a Service (IaaS),SaaS (Software as a Service)
3 main approaches of Saving and Reloading an ML Model-Pickle Approach,Joblib Approach,JSON approach
https://www.datacamp.com/community/tutorials/pickle-python-tutorial https://github.com/balavenkatesh3322/model_deployment
1.Azure
2.Heroku
3.Amazon Web Services Everything AWS https://app.polymersearch.com/discover/aws
4.Google cloud platform
5.ngrok https://www.youtube.com/watch?v=AkEnjJ5yWV0
Deploy a Machine Learning Model for Free https://www.freecodecamp.org/news/deploy-your-machine-learning-models-for-free/
mlpack is a fast, flexible machine learning library suitable for both data science prototyping and deployment https://numfocus.org/project/mlpack https://github.com/mlpack/mlpack
MODEL DEPLOYMENT USING TF SERVING
Dockerize https://www.kdnuggets.com/2021/04/dockerize-any-machine-learning-application.html https://pub.towardsai.net/how-to-dockerize-your-data-science-project-a-quick-guide-b6fa2d6a8ba1
bodywork-core MLOps tool for deploying machine learning projects to Kubernetes https://github.com/bodywork-ml/bodywork-core
Create ML model inside the docker container https://dev.to/niteshthapliyal/create-ml-model-inside-the-docker-container-3b23
LyftLearn: ML Model Training Infrastructure built on Kubernetes https://eng.lyft.com/lyftlearn-ml-model-training-infrastructure-built-on-kubernetes-aef8218842bb
Model Serving https://neptune.ai/blog/ml-model-serving-best-tools?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-ml-model-serving-best-tools
TensorFlow Extended (TFX) is an end-to-end platform for deploying production ML pipelines https://www.tensorflow.org/tfx https://theaisummer.com/tfx/?utm_content=163294295&utm_medium=social&utm_source=linkedin&hss_channel=lcp-42461735
torchblaze https://github.com/MLH-Fellowship/torchblaze https://mlh-fellowship.github.io/torchblaze/
ML Aide Manage Machine Learning Lifecycle https://mlaide.com/home https://medium.com/ml-aide/manage-machine-learning-lifecycle-with-ml-aide-dfe7710cbe53
Models visualization using Tensorboard,netron, TensorBoard.dev
Python web Frameworks for App Development- Flask,Streamlit,fastapi,fastDeploy,Django,Web2py,Pyramid,CherryPy,Voila,Kivy and Kivymd
streamlit,Gradio,mia,opyrator,plotly jupyterdash,h2o wave,dash,gradio,PyWebIO,r shiny,sanic,panel,flask,django,PySimpleGUI,pywebio,autocalc,Mercury,Chitra ,Bokeh,Panel,jupyter Voila with ipywidgets,Panel,dash,Fast Dash,BentoML,Cortex,Seldon,UnionML,Taipy,fastDeploy,Mlflow,Seldon core,tensorflow serving,kserve,torchserve,ray,clearml,mlrun,pymlpipe,FastDeploy,Shiny,Voila,Cog,BentoML,MLflow,PyMLpipe,truss,playtorch,Streamsync,panel,Databutton,plotly,pyscript, Sanic,skops,Mage,sematic,Cog, BentoML,Truss,bentoctl,Banana,Pyramid,Docker,Kubernetes,SageMaker,TensorFlow Serving,Kubeflow,Cortex,Seldon.io,Cortex,TensorFlow Serving,TorchServe,KFServing,Multi Model Server,Triton Inference Server,ForestFlow,Seldon Core,BudgetML,GraphPipe,Hydrosphere,MLEM,Opyrator,Apache PredictionIO,Cortex,Triton Inference Server,ForestFlow,DeepDetect,Seldon Core,Kubeflow,datapane,Pynecone.io,Anvil,h2oai nitro,rest-model-service,Databutton,CherryPy,Anvil,modelbit,Pynecone,modelbit,wagtail,flet,Chainlit,Solara
Django models https://www.deploymachinelearning.com/#create-django-models https://www.deploymachinelearning.com/
BentoML https://github.com/bentoml/BentoML
UnionML: the easiest way to build and deploy machine learning microservices https://github.com/unionai-oss/unionml
panel high-level app and dashboarding solution for Python https://github.com/holoviz/panel
sanic https://github.com/sanic-org/sanic
Gradio - take input from user https://gradio.app/getting_started
Fast Dash https://fastdash.app/
binder - https://mybinder.org/
Netlify https://www.analyticsvidhya.com/blog/2021/04/easily-deploy-your-machine-learning-model-into-a-web-app-netlify/
streamlit https://www.kdnuggets.com/2019/10/write-web-apps-using-simple-python-data-scientists.html https://www.youtube.com/watch?v=iUgNIFrVejc https://blog.streamlit.io/introducing-theming/
Streamlit Flask App from Colab using remoteit and ngrok https://www.youtube.com/watch?v=O2enoygZwl4
Streamlit to databases https://docs.streamlit.io/en/0.83.0/tutorial/databases.html
https://github.com/jrieke/best-of-streamlit
https://neptune.ai/blog/streamlit-guide-machine-learning?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-streamlit-guide-machine-learning
streamlit-ace https://github.com/okld/streamlit-ace https://www.youtube.com/watch?v=Iv2vt-7AYNQ
customize the themes of your Streamlit web apps https://www.youtube.com/watch?v=3xJYP_C4KNE https://github.com/khuyentran1401/Data-science/tree/master/applications/pywebio_examples
colab_everything Python library to run streamlit, flask, fastapi, etc on google colab https://github.com/Ankur-singh/colab_everything/
dash https://github.com/plotly/dash
panel-highcharts https://awesome-panel.org/ https://github.com/marcskovmadsen/panel-highcharts https://github.com/holoviz/panel https://github.com/holoviz/panel
opyrator Turns your machine learning code into microservices with web API, interactive GUI, and more https://github.com/ml-tooling/opyrator
plotly https://plotly.com/ https://analyticsindiamag.com/how-to-use-plotly-in-colab/
Creating a Machine Learning App with Power BI and PyCaret
Streamlit vs. Dash vs. Shiny vs. Voila vs. Flask vs. Jupyter vs django vs PySimpleGUIvs pywebio vs Gradio vs autocalc vs Mercury vs Chitra https://www.datarevenue.com/en-blog/data-dashboarding-streamlit-vs-dash-vs-shiny-vs-voila,pymlpipe,Lightning Apps,Aibro
Mercury: easily convert Python notebook to web app and share with others https://github.com/mljar/mercury
autocalc https://github.com/kefirbandi/autocalc https://towardsdatascience.com/creating-a-ui-with-ipywidgets-and-autocalc-2ef8ea4cc6c2
Quickly deploy ML WebApps https://ngrok.com/
Chitra https://github.com/gradsflow/chitra
Deepnote https://deepnote.com/ https://www.youtube.com/watch?v=0ppptVxgEI8
booklet https://booklet.ai/ https://towardsdatascience.com/building-a-covid-19-project-recommendation-system-4607806923b9
https://analyticsindiamag.com/top-8-python-tools-for-app-development/
Voila This library can turn your Jupyter notebooks into standalone web apps that can be deployed to any cloud platform. https://voila.readthedocs.io/en/stable/
H2O.ai https://www.h2o.ai/blog/data-to-production-ready-models-to-business-apps-in-just-a-few-steps/
PyQt and Tkinter , PySimpleGUI are GUI programming in Python https://github.com/tirthajyoti/DS-with-PySimpleGUI
DearPyGui https://github.com/hoffstadt/DearPyGui
PySimpleGUI https://github.com/PySimpleGUI/PySimpleGUI
Gooey Turn (almost) any Python command line program into a full GUI application with one line https://github.com/chriskiehl/Gooey
snapyml Deploy AI Models For Free -http://snapyml.snapy.ai/
BentoML https://github.com/bentoml/BentoML
h20 wave-apps https://github.com/h2oai/wave-apps https://h2oai.github.io/wave/docs/installation/ https://h2oai.github.io/wave/
h20 Wave ML (AutoML for Wave Apps) https://h2oai.github.io/wave/blog/ml-release-0.3.0/
fastapi https://towardsdatascience.com/deploying-ml-models-in-production-with-fastapi-and-celery-7063e539a5db
FastAPI + Uvicorn https://www.kdnuggets.com/2021/04/deploy-machine-learning-models-to-web.html
FastAPI based template https://github.com/99sbr/fastapi-template fastapi-log 0.0.3 https://pypi.org/project/fastapi-log/
testing FastAPI ML APIs with Locust https://locust.io/ https://rubikscode.net/2022/03/21/performance-testing-fastapi-ml-apis-with-locust/
chitra 𝗖𝗿𝗲𝗮𝘁𝗲 𝗔𝗣𝗜 𝗳𝗼𝗿 𝗔𝗻𝘆 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹 https://github.com/aniketmaurya/chitra
PyWebIO Write Interactive Web App in Script Way Using Python https://www.youtube.com/watch?v=vp1ZNapAy6Y https://towardsdatascience.com/pywebio-write-interactive-web-app-in-script-way-using-python-14f50155af4e https://github.com/tirthajyoti/PyWebIO
aibro Deploy Machine Learning Models to the Cloud Quickly and Easily https://aipaca.ai/?ref=hackernoon.com https://medium.datadriveninvestor.com/how-to-deploy-machine-learning-models-to-the-cloud-quickly-and-easily-41cca9425c75
Katana https://github.com/shaz13/katana https://katana-demo.herokuapp.com/redoc https://katana-demo.herokuapp.com/docs
DS-with-PySimpleGUI https://github.com/tirthajyoti/DS-with-PySimpleGUI
pywinauto Windows GUI Automation with Python
tkinter to deploy machine learning model-https://analyticsindiamag.com/complete-tutorial-on-tkinter-to-deploy-machine-learning-model/
Tkinter-Designer Create Beautiful Tkinter GUIs by Drag and Drop https://github.com/ParthJadhav/Tkinter-Designer
Web-Based GUI (Gradio)- https://analyticsindiamag.com/guide-to-gradio-create-web-based-gui-applications-for-machine-learning/ https://www.gradio.app/
Bamboolib https://medium.com/ai-in-plain-english/bamboolib-a-data-warriors-weapon-9f734f4c2553
web application(dash)- https://dash.plotly.com/
Pyramid web framework https://trypyramid.com/documentation.html
Kivy /Kivymd creating an android app
https://towardsdatascience.com/pycaret-2-1-is-here-whats-new-4aae6a7f636a
Create a Website with AI https://www.bookmark.com/
localhost to globalurl https://ngrok.com/ https://remote.it/
Jupyter Notebook into an interactive dashboard (voila)-https://voila.readthedocs.io/en/stable/
high-level app and dashboarding solution(Panel)-https://panel.holoviz.org/
MaaS Build ML Models As A Service https://www.analyticsvidhya.com/blog/2021/05/maas-build-ml-models-as-a-service/
https://github.com/gradio-app/gradio
Tensorflow lite:Use of tensorflow lite to reduce size of model https://www.tensorflow.org/lite https://codelabs.developers.google.com/codelabs/recognize-flowers-with-tensorflow-on-android-beta/#0 https://tfhub.dev/s?deployment-format=lite https://www.tensorflow.org/lite/examples https://www.tensorflow.org/lite/microcontrollers https://www.tensorflow.org/lite/models
Adventures-in-TensorFlow-Lite https://github.com/sayakpaul/Adventures-in-TensorFlow-Lite
coral https://coral.ai/docs/edgetpu/models-intro/
TF Micro and SensiML https://blog.tensorflow.org/2021/05/building-tinyml-application-with-tf-micro-and-sensiml.html
six different types of methods:
-
Pruning, Weight sharing Structured Pruning,Unstructured Pruning,Pruning Local,Global Pruning Pruning criteria( Weight magnitude criterion,Gradient magnitude pruning,Global or local pruning, Model Pruning: Remove irrelevant edges and nodes from a network. Three popular types of pruning: Zero pruning Activation pruning Redundancy pruning
-
Quantization ,TensorFlow Quantum, Int8 quantization Post-Training Quantization — Reduce Float16 — Hybrid Quantization — Integer Quantization -dynamic range quantization
- Dynamic/Runtime Quantization
- Post-Training Static Quantization
- Static Quantization-aware Training (QAT)
- During-Training Quantization
- Post-Training Pruning
- Post-Training Clustering
-
Knowledge distillation
-
Parameter sharing
-
Tensor decomposition
-
Linear Transformer,Winograd Transformation
-
Selective attention
-
Low-rank factorisation
-
brevitas https://github.com/Xilinx/brevitas/
Structured pruning,Unstructured/semi-structured pruning,Quantization,Distillation,Post Training,Training-Aware,Sparse Transfer
AIMET is a library that provides advanced quantization and compression techniques for trained neural network models. https://github.com/quic/aimet
Pruning,Nonstructural pruning,Structural pruning,Quantisation-Aware Training,Post-Training Quantisation
QKeras: a quantization deep learning library for Tensorflow Keras
Model Compression https://github.com/open-mmlab/mmrazor
Knowledge Distillation knowledge are categorized into three different types: Response-based knowledge, Feature-based knowledge, and Relation-based knowledge three principal types of methods for training student and teacher models, namely offline, online and self distillation.
Distillation library KD_Lib https://github.com/SforAiDl/KD_Lib
ibm new tool https://www.zdnet.com/article/ibms-new-tool-lets-developers-add-quantum-computing-power-to-machine-learning/
qiskit-machine-learning https://github.com/Qiskit/qiskit-machine-learning https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.neural_networks.SamplingNeuralNetwork.html
compressors https://github.com/elephantmipt/compressors
poniard scikit-learn model comparison https://github.com/rxavier/poniard
https://rachitsingh.com/deep-learning-model-compression/#quantization
model optimization (architecture)
TF Lite with iOS, Swift and TF Lite Swift
TinyML https://blog.tensorflow.org/2020/08/the-future-of-ml-tiny-and-bright.html
tinyml-papers-and-projects This is a list of interesting papers and projects about TinyML https://github.com/gigwegbe/tinyml-papers-and-projects
pennylane Python library for differentiable programming of quantum computers https://github.com/PennyLaneAI/pennylane
AI Engine for Edge Devices https://github.com/johnolafenwa/deepstack TensorFlow Lite Samples on Unity https://github.com/asus4/tf-lite-unity-sample
tflite-support TFLite Support is a toolkit that helps users to develop ML and deploy TFLite models onto mobile / ioT devices https://github.com/tensorflow/tflite-support
Post-training Quantization in TensorFlow Lite https://www.tensorflow.org/lite/performance/post_training_quantization
pruning
Custom Text Classification on Android using TensorFlow Lite https://www.analyticsvidhya.com/blog/2021/05/custom-text-classification-on-android-using-tensorflow-lite/
aimet advanced quantization and compression techniques for trained neural network models https://github.com/quic/aimet https://github.com/quic/aimet-model-zoo
Automatic Model Compression (AutoMC) framework for developing smaller and faster AI applications https://github.com/Tencent/PocketFlow
leverage of model architecture
Federated Learning https://www.analyticsvidhya.com/blog/2021/04/federated-learning-for-beginners/ https://www.tensorflow.org/federated
FEDERATED LEARNING(Centralized, Decentralized, Heterogeneous) https://blog.openmined.org/federated-learning-types/ https://aman.ai/primers/ai/federated-learning/
Federated Learning with FEDn https://github.com/scaleoutsystems/fedn
plato scalable federated learning research framework https://github.com/TL-System/plato
FedNLP: A Research Platform for Federated Learning in Natural Language Processing https://github.com/FedML-AI/FedNLP
privacy https://github.com/tensorflow/privacy
Differential Privacy https://aman.ai/primers/ai/differential-privacy/
Quantization:Use Quantization to reduce size of model https://medium.com/qiskit/introducing-qiskit-machine-learning-5f06b6597526
Post Training Quantization Aware Training Quantization
TensorFlow Quantum https://www.tensorflow.org/quantum
Qiskit Machine Learning https://github.com/Qiskit/qiskit-machine-learning
Quantum Machine Learning
Quantum Kernels https://github.com/Qiskit/qiskit-machine-learning/blob/master/docs/tutorials/03_quantum_kernel.ipynb
IBMs Qiskit,Google’s Cirq,Amazon’s AWS Braket,Microsoft’s Q# and Azure Quantum,Rigetti’s Forest,Xanadu’s Pennylane
On-Device Machine Learning https://developers.google.com/learn/topics/on-device-ml https://www.tensorflow.org/lite/guide/model_maker
Core ML for iOS, Tensorflow lite for Android, ML.NET for Windows and ML Kit
8.Mointoring model
CI CD pipeline used- circleci , jenkins
In real world project use pipeline -https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html
1.easy debugging
2.better readability
Types of Data Drift
Concept drift,Virtual drift,Covariate shift,Prior probability shift,Annotator drift,Data poisoning
There are several measures you can take to mitigate the effects of data drift:
Regular retraining,Data preprocessing,Data augmentation,Monitoring,Online learning,Domain adaptation,Annotator and data quality control
Techniques for Detecting Data Drift
There are several techniques currently available for detecting data drift in machine learning:
Data visualization tools,Drift detection methods,Data quality control techniques,Drift detection libraries,Auto-ML tools
BIG DATA: hadoop,apache spark
project structure
data science project structure https://towardsdatascience.com/automate-your-data-science-project-structure-in-three-easy-steps-277c92328d24
research paper-https://arxiv.org/ ,https://arxiv.org/list/cs.LG/recent, https://www.kaggle.com/Cornell-University/arxiv
arXiv.org https://arxiv.org/list/cs.AI/recent https://arxiv.org/list/stat.ML/recent https://arxiv.org/list/cs.CL/recent https://arxiv.org/list/cs.CV/recent
https://github.com/amitness/papers-with-video
Datasets on arXiv https://medium.com/paperswithcode/datasets-on-arxiv-1a5a8f7bd104
code for research paper https://www.analyticsvidhya.com/blog/2021/06/steal-the-code-ethically-get-better-at-ml-ai-research/
papertalk https://papertalk.org/index
connected papers https://www.connectedpapers.com/
Stanford AI Lab Papers and Talks at ICLR 2021 https://ramseyelbasheer.io/2021/05/03/stanford-ai-lab-papers-and-talks-at-iclr-2021/
Semantic Scholar searches: https://www.semanticscholar.org/search?q=%22neural%20networks%22&sort=relevance&ae=false
https://www.semanticscholar.org/search?q=%22machine%20learning%22&sort=relevance&ae=false
https://www.semanticscholar.org/search?q=%22natural%20language%22&sort=relevance&ae=false
https://www.semanticscholar.org/search?q=%22computer%20vision%22&sort=relevance&ae=false
https://www.semanticscholar.org/search?q=%22deep%20learning%22&sort=relevance&ae=false
code for Research Papers-https://chrome.google.com/webstore/detail/find-code-for-research-pa/aikkeehnlfpamidigaffhfmgbkdeheil
Summarise Research Papers - https://www.semanticscholar.org/
Structure Your Data Science Projects https://towardsdatascience.com/structure-your-data-science-projects-6c6c8653c16a
programming language for data science is Python,R,Julia,Java,Scala,JAVA SCRIPT(Tensorflow.js),etc...
IDE:jupyter notebook,spyder,pycharm,visual studio
4 Tools for Reproducible Jupyter Notebooks https://towardsdatascience.com/4-tools-for-reproducible-jupyter-notebooks-d7423721bd04
12 Jupyter Notebook Extensions That Will Make Your Life Easier https://towardsdatascience.com/12-jupyter-notebook-extensions-that-will-make-your-life-easier-e0aae0bd181
Coding Tools Powered by AI : GitHub Co-Pilot,Tabnine,AI2SQL,Mutable,MarsXm,Ghostwriter,Stenography,OpenAI Codex,CodeT5,Polycoder,GhostWriter Replit,Seek,AI2SQL,Cody by Sourcegraph,MutableAI,StableCode,DeciCoder,santacoder,Code Llama,Amazon CodeWhisperer,Bagasura
BEST ONLINE COURSES
1.COURSERA
2.UDEMY
3.EDX
4.DATACAMP
5.Udacity
6.https://www.skillbasics.com/
BEST YOUTUBE CHANNEL TO FOLLOW
1.Krish Naik-https://www.youtube.com/user/krishnaik06
2.Codebasics-https://www.youtube.com/channel/UCh9nVJoWXmFb7sLApWGcLPQ
3.Abhishek thakur-https://www.youtube.com/user/abhisheksvnit
4.AIEngineering-https://www.youtube.com/channel/UCwBs8TLOogwyGd0GxHCp-Dw
5.Ineuron-https://www.youtube.com/channel/UCb1GdqUqArXMQ3RS86lqqOw
6.Ken jee-https://www.youtube.com/c/KenJee1/featured
7.3Blue1Brown-https://www.youtube.com/c/3blue1brown/featured
8.The AI Guy -https://www.youtube.com/channel/UCrydcKaojc44XnuXrfhlV8Q
9.Unfold Data Science-https://www.youtube.com/channel/UCh8IuVJvRdporrHi-I9H7Vw etc...
BEST BLOGS TO FOLLOW
https://www.cybrhome.com/topic/data-science-blogs
AI Summary https://ai-summary.com/
https://www.datasciencecentral.com/profiles/blog/list https://developer.nvidia.com/blog/?ncid=em-prom-48627
1.Towards data science-https://towardsdatascience.com/
2.Analyticsvidhya-https://www.analyticsvidhya.com/blog/?utm_source=feed&utm_medium=navbar https://analyticsindiamag.com/ https://www.analyticsinsight.net/
3.Medium-https://medium.com/
4.Machinelearningmastery-https://machinelearningmastery.com/blog/
5.ML+ -https://www.machinelearningplus.com/
6.analyticsinsight https://www.analyticsinsight.net/category/latest-news/ https://www.analyticsinsight.net/
7.KDnuggets https://www.kdnuggets.com/ https://www.kdnuggets.com/news/index.html
8.Artificial Intelligence Database https://www.wired.com/category/artificial-intelligence/?verso=true
https://machinelearningknowledge.ai/
https://github.com/rushter/data-science-blogs
https://www.datamuni.com/
https://blog.ml.cmu.edu/?utm_source=towardsai.net&utm_medium=referral&utm_campaign=marketing&utm_term=machine-learning-blog&utm_content=best-machine-learning-blogs-to-follow
https://www.amazon.science/blog?utm_source=towardsai.net&utm_medium=referral&utm_campaign=marketing&utm_term=machine+learning+blog&utm_content=machine+learning+blog&f0=0000016e-2ff1-d205-a5ef-aff9651e0000&s=0
https://distill.pub/?utm_source=towardsai.net&utm_medium=referral&utm_campaign=marketing&utm_term=machine-learning-blog&utm_content=best-machine-learning-blogs-to-follow
https://ai.googleblog.com/search/label/Machine%20Learning?utm_source=towardsai.net&utm_medium=referral&utm_campaign=marketing&utm_term=machine-learning-blog&utm_content=best-machine-learning-blogs-to-follow
https://neptune.ai/blog?utm_source=towardsai.net&utm_medium=referral&utm_campaign=marketing&utm_term=machine+learning+blog&utm_content=machine+learning+blog
https://bair.berkeley.edu/blog/?utm_source=towardsai.net&utm_medium=referral&utm_campaign=marketing&utm_term=machine-learning-blog&utm_content=best-machine-learning-blogs-to-follow
https://deepmind.com/research?utm_source=towardsai.net&utm_medium=referral&utm_campaign=marketing&utm_term=machine-learning-blog&utm_content=machine-learning-blogs-to-follow&filters=%7B%22category%22:%5B%22Research%22%5D%7D
https://ai.facebook.com/blog/?utm_source=towardsai.net&utm_medium=referral&utm_campaign=marketing&utm_term=machine-learning-blog&utm_content=machine-learning-blogs-to-follow
https://becominghuman.ai/top-25-ai-and-machine-learning-blogs-for-data-scientists-9f121bcfd9a2
https://medium.com/towards-artificial-intelligence/best-machine-learning-blogs-to-follow-ml-research-ai-3994e01967f9
BEST RESOURCES
https://amitness.com/toolbox/ https://khuyentran1401.github.io/Data-science/ https://github.com/ml-tooling/best-of-ml-python
https://github.com/ml-tooling/best-of-ml-python#machine-learning-frameworks http://dfkoz.com/ai-data-landscape/ https://landscape.lfai.foundation/
https://towardsdatascience.com/data-science-tools-f16ecd91c95d https://mathdatasimplified.com/ https://github.com/neomatrix369/awesome-ai-ml-dl
https://amitness.com/ https://postsyoumighthavemissed.com/search/
1.paperswithcode-https://paperswithcode.com/methods https://www.paperswithcode.com/datasets
paperswithcode-client https://github.com/paperswithcode/paperswithcode-client https://paperswithcode.com/lib/torchvision
2.madewithml-https://madewithml.com/topics/ https://madewithml.com/courses/applied-ml-in-production/ https://github.com/GokuMohandas/applied-ml
modelzoo https://modelzoo.co/
Weights & Biases- https://wandb.ai/gallery sotabench-https://sotabench.com/
3.Deep learning-https://course.fullstackdeeplearning.com/#course-content
4.pytorch deep learning-https://atcold.github.io/pytorch-Deep-Learning/
PYTORCH HUB https://pytorch.org/hub/ https://pytorch.org/hub/research-models
5.https://papers.labml.ai/papers/daily https://42papers.com/
https://www.kdnuggets.com/2019/08/pytorch-cheat-sheet-beginners.html https://www.kdnuggets.com/2019/04/nlp-pytorch.html https://www.kdnuggets.com/2019/08/9-tips-training-lightning-fast-neural-networks-pytorch.html
fairscale PyTorch extensions for high performance and large scale training https://github.com/facebookresearch/fairscale
PyTorch Lightning-https://github.com/PyTorchLightning/pytorch-lightning https://www.kdnuggets.com/2020/11/deploy-pytorch-lightning-models-production.html
PYTORCH - https://pytorch.org/ https://pytorch.org/ecosystem/ https://pytorch.org/tutorials/ https://pytorch.org/docs/stable/index.html https://github.com/pytorch/pytorch
PYTORCH Lightning https://pytorchlightning.ai/community#projects https://seannaren.medium.com/introducing-pytorch-lightning-sharded-train-sota-models-with-half-the-memory-7bcc8b4484f2
ort Accelerate PyTorch models with ONNX Runtime https://github.com/pytorch/ort
lightning-flash https://github.com/PyTorchLightning/lightning-flash https://pytorch-lightning.medium.com/introducing-lightning-flash-the-fastest-way-to-get-started-with-deep-learning-202f196b3b98
torchflare easy-to-use PyTorch Framework https://github.com/Atharva-Phatak/torchflare
Lightning Bolts collection of well established, SOTA models and components https://github.com/PyTorchLightning/lightning-bolts
Sharded: A New Technique To Double The Size Of PyTorch Models https://towardsdatascience.com/sharded-a-new-technique-to-double-the-size-of-pytorch-models-3af057466dba
𝗢𝗽𝗮𝗰𝘂𝘀 (𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗣𝘆𝗧𝗼𝗿𝗰𝗵 𝗺𝗼𝗱𝗲𝗹𝘀 𝘄𝗶𝘁𝗵 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝗹 𝗽𝗿𝗶𝘃𝗮𝗰𝘆)-https://opacus.ai/
light-face-detection https://github.com/borhanMorphy/light-face-detection
DALLE-pytorch https://github.com/lucidrains/DALLE-pytorch
PyTorch JIT -https://lernapparat.de/jit-optimization-intro/
jax- https://github.com/google/jax
incubator-mxnet - https://github.com/apache/incubator-mxnet
ignite-https://github.com/pytorch/ignite
fastText - https://github.com/facebookresearch/fastText
rapidminer-https://rapidminer.com/
5.deep-learning-drizzle-https://deep-learning-drizzle.github.io/ https://deep-learning-drizzle.github.io/index.html
6.Fastaibook-https://github.com/fastai/fastbook , https://course.fast.ai/ https://www.fast.ai/2019/07/08/fastai-nlp/ https://www.fast.ai/2020/08/21/fastai2-launch/
neptune.ai-https://docs.neptune.ai/index.html
Dive into Deep Learning http://d2l.ai/
7.TopDeepLearning-https://github.com/aymericdamien/TopDeepLearning
8.NLP-progress-https://github.com/sebastianruder/NLP-progress
9.EasyOCR,textract,pytesseract,tesserocr,Amazon textract,TabulaPy, pyzbar,pyocr,OCR With Detectron2,PymuPDF,Camelot,keras ocr,Keras CRNN,PDFTableExtract(by PyPDF2),tesseract-ocr,PyMuPDF,pyocr,Apache Tika,pdfPlumber,PDFMiner,PyPDF2,pdfMiner3,pdf2image,pdfquery,TextOCR,keras-CTPN,pytorch-CTPN,ocr.pytorch,layout-parser,tabula,Spark OCR,mmocr,Amazon Rekognition ,Amazon Textract,Azure OCR, Google OCR,PaddleOCR,TrOCR,MMOCR,awesome OCR,Paddle OCR,OCRmyPDF,calamari, attention ocr,Mozart,pdftabextract,Doc2Text,OpenCV’s EAST,deepdoctection,EAST text detector,slate3k,textract,CRAFT-pytorch,ocr donut,LOGOS ocr, ocrpy,docquery,Parsr,DocuQuery,LayoutLM,docTR,docquery,CascadeTabNet,OpenCV,OCRopus,Kraken,OCRmypdf,MMOCR,PPOCR,Keras-OCR,MultiOcr,TrOCR,docTR,surya OCR,Bhashini,OCRopus,Kraken
Processing documents as Text: extract text with PyPDF2, extract tables with Camelot or TabulaPy, extract figures with PyMuPDF.
Converting documents into Image (OCR): conversion with pdf2image, extract data with PyTesseract plus many other supporting libraries, or just LayoutParser.
OCR toolbox from Davar-Lab https://github.com/hikopensource/davar-lab-ocr
To pdf: python-pdfkit,wkhtmltopdf,FPDF
10.Awesome-pytorch-list-https://github.com/bharathgs/Awesome-pytorch-list https://shivanandroy.com/awesome-nlp-resources/
11.free-data-science-books-https://github.com/chaconnewu/free-data-science-books
12.arcgis-https://github.com/Esri/arcgis-python-api https://geemap.org/
13.data-science-ipython-notebooks-https://github.com/donnemartin/data-science-ipython-notebooks
14.julia-https://github.com/JuliaLang/julia , https://docs.julialang.org/en/v1/
15.google-research-https://github.com/google-research/google-research
16.reinforcement-learning-https://github.com/dennybritz/reinforcement-learning
17.keras-applications-https://github.com/keras-team/keras-applications , https://github.com/keras-team/keras https://keras.io/examples/
18.opencv-https://github.com/opencv/opencv
19.transformers-https://github.com/huggingface/transformers
20.code implementations for research papers-https://chrome.google.com/webstore/detail/find-code-for-research-pa/aikkeehnlfpamidigaffhfmgbkdeheil
21.regarding satellite images - Geo AI,Arcgis,geemap
ersi arcgis-https://www.esri.com/en-us/arcgis/about-arcgis/overview
earthcube-https://www.earthcube.eu/
geemap-https://geemap.org/
22.Monk_Object_Detection-https://github.com/Tessellate-Imaging/Monk_Object_Detection
https://github.com/Tessellate-Imaging/monk_v1
pyradox https://github.com/Ritvik19/pyradox
23.NLP-progress - https://github.com/sebastianruder/NLP-progress
24.interview-question-data-science-https://github.com/iNeuronai/interview-question-data-science-
27.Tool for visualizing attention in the Transformer model-https://github.com/jessevig/bertviz
28.TransCoder-https://github.com/facebookresearch/TransCoder
29.Tessellate-Imaging-https://github.com/Tessellate-Imaging/monk_v1
Monk_Object_Detection-https://github.com/Tessellate-Imaging/Monk_Object_Detection/tree/master/application_model_zoo
Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials- https://github.com/TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials
30.Machine-Learning-with-Python-https://github.com/tirthajyoti/Machine-Learning-with-Python
31.huggingface contain almost all nlp pretrained model and all tasks related to nlp field https://huggingface.co/course/chapter0?fw=pt
https://huggingface.co/models https://www.kdnuggets.com/2021/02/hugging-face-transformer-basics.html https://huggingface.co/modelsz
https://github.com/huggingface https://github.com/huggingface/transformers https://huggingface.co/transformers/ https://huggingface.co/transformers/master/ https://github.com/huggingface/tokenizers
hugging face spaces https://huggingface.co/spaces
Hugging Face pipelines https://towardsdatascience.com/effortless-nlp-using-pre-trained-hugging-face-pipelines-with-just-3-lines-of-code-a4788d95754f
Fine-tuning pretrained NLP models with Huggingface’s Trainer https://towardsdatascience.com/fine-tuning-pretrained-nlp-models-with-huggingfaces-trainer-6326a4456e7b
Mixing Hugging Face Models with Gradio 2.0 https://gradio.app/blog/using-huggingface-models https://huggingface.co/blog/gradio
ktrain https://github.com/amaiya/ktrain
Top 6 Alternatives To Hugging Face https://analyticsindiamag.com/top-6-alternatives-to-hugging-face/
32.multi-task-NLP-https://github.com/hellohaptik/multi-task-NLP
33.gpt-2 - https://github.com/openai/gpt-2
34.Powerful and efficient Computer Vision Annotation Tool (CVAT)-https://github.com/openvinotoolkit/cvat, https://github.com/abreheret/PixelAnnotationTool
https://github.com/UniversalDataTool/universal-data-tool http://www.robots.ox.ac.uk/~vgg/software/via/
36.awesome Data Science-https://github.com/academic/awesome-datascience
39.Super Duper NLP Repo-https://notebooks.quantumstat.com/ https://models.quantumstat.com/ https://miro.com/app/board/o9J_kqndLls=/ https://datasets.quantumstat.com/
https://index.quantumstat.com/
40.papers summarizing the advances in the field-https://github.com/eugeneyan/ml-surveys
41.deep-translator-https://github.com/nidhaloff/deep-translator
44.ipython-sql-https://github.com/catherinedevlin/ipython-sql
45.libra-https://github.com/Palashio/libra
46.opencv-https://github.com/opencv/opencv
47.learnopencv-https://github.com/spmallick/learnopencv , https://www.learnopencv.com/
48.math is fun-https://www.mathsisfun.com/ , https://pabloinsente.github.io/intro-linear-algebra, https://hadrienj.github.io/posts/Deep-Learning-Book-Series-Introduction/
49.DEEP LEARNING WITH PYTORCH: A 60 MINUTE BLITZ - https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html
50.https://data-flair.training/blogs/
https://data-flair.training/blogs/python-tutorials-home/ https://data-flair.training/blogs/hadoop-tutorials-home/ https://data-flair.training/blogs/spark-tutorials-home/
https://data-flair.training/blogs/tableau-tutorials-home/ https://data-flair.training/blogs/data-science-tutorials-home/
Spark Release 3.0.1-https://spark.apache.org/releases/spark-release-3-0-1.html https://neptune.ai/blog/apache-spark-tutorial
Koalas on Apache Spark - Pandas API https://www.youtube.com/watch?v=kOtAMiMe1JY&t=482s https://koalas.readthedocs.io/en/latest/
mllib https://spark.apache.org/docs/2.0.0/api/python/pyspark.mllib.html https://spark.apache.org/docs/2.0.0/api/python/index.html
https://data-flair.training/blogs/spark-tutorial/ Spark Core,Spark SQL,Spark Streaming,Spark MLlib,Spark GraphX,etc...
Machine Learning with Optimus on Apache Spark https://www.kdnuggets.com/2017/11/machine-learning-with-optimus.html
BigDL: Distributed Deep Learning Framework for Apache Spark https://github.com/intel-analytics/BigDL
51.for more cheatsheets-https://github.com/FavioVazquez/ds-cheatsheets , https://medium.com/swlh/the-ultimate-cheat-sheet-for-data-scientists-d1e247b6a60c
https://www.theinsaneapp.com/2020/12/machine-learning-and-data-science-cheat-sheets-pdf.html
https://stanford.edu/~shervine/teaching/cs-229/cheatsheet-supervised-learning
52.text2emotion-https://pypi.org/project/text2emotion/
53.ExploriPy-https://analyticsindiamag.com/hands-on-tutorial-on-exploripy-effortless-target-based-eda-tool/
54.TCN-https://github.com/philipperemy/keras-tcn
56.earthengine-py-notebooks-https://github.com/giswqs/earthengine-py-notebooks
58.numerical-linear-algebra -https://github.com/fastai/numerical-linear-algebra
61.chatbot- from scratch,google dialogflow,rasa nlu,azure luis, Azure Bot Service,chatterbot,Amazon lex,Wit.ai,Luis.ai,IBM Watson,Parrot etc...
Chatterbot,Botkit,BotPress,Bottender,IBM Watson,Microsoft bot Framework,Pandorabots,RASA Stack,Pandorabots,BlenderBot3,DeepPavlov,OpenDialogTock,Wit.ai, Pandorabots,Proto AIC,HubSpot Chatbot Builder,Intercom,Zendesk,Freshworks,Botsify,Tidio,Infobip,OpenChat
ChatGPT openai chatboat and search engine,meta ChatLLaMA ,VisualChatGPT,ViperGPT,GPT-4,AutoGPT,babyagi,ChaosGPT,Agentgpt,MiniGPT-4,GPT4 All ,BabyAGI and Auto-GPT,Dolly,Camel,claude2,bing,Code Interpreter,Anthropic's,WizardCoder
Bard google chatboat and search engine,PALM API,OpenChatKit: Open-Source ChatGPT Alternative
meta LLaMA,LLaMA-v2,Alpaca 7B,h2o-llmstudio,StableLM,HuggingChat
Ernie bot,Baidu chatbot,Claude,Alpaca,ChatGLM,Bloomberg-GPT,Vicuna,StackLLaMA,h2o-llmstudio,Claude 2,Perplexity Ai,FreeWilly1,FreeWilly2,Falcon,Dolly,Guanaco,BloomZ,Alpaca,OpenChatKit,GPT4ALL,Vicuna,Flan-T5,FalconLite ,StableBeluga2,Tongyi Qianwen
no code chatbots https://juji.io/
https://github.com/fendouai/Awesome-Chatbot https://medium.com/nerd-for-tech/make-money-building-a-fast-powerful-chatbot-in-10-minutes-using-nltk-91038e15ab17
https://www.analyticsinsight.net/category/chatbots/ https://www.promaticsindia.com/blog/here-are-the-most-popular-chatbot-development-frameworks/
https://blog.ubisend.com/optimise-chatbots/chatbot-training-data
OpenChat: Open Source Chatting Framework for Generative Models https://analyticsindiamag.com/a-brief-overview-of-openchat-open-source-chatting-framework-for-generative-models/
- No Code Machine Learning / Deep Learning https://analyticsindiamag.com/top-12-no-code-machine-learning-platforms-in-2021/ https://www.pye.ai/2021/06/01/2021-list-of-top-data-science-platforms-end-to-end-machine-learning/
https://serokell.io/blog/top-no-code-platforms https://www.nanalyze.com/2021/04/no-code-platforms-machine-learning/
Akkio, Obviously.ai, DataRobot, Levity, Clarifai, Teachable Machines, Lobe,pimer,DynaBench,APAflow,Runway AI,Obviously AI,CreateML,MakeML,Fritz AI,MonkeyLearn,Nanonets,SuperAnnotate,CausaLens,Levity,Clarifai,BigML,Teachable Machine,actable,Bonsai,labelsleuth,Cooka,oracle AutoML,EdgeImpulse,Mantium AI,Sway,Graphite,DataRobot,Graphite Note,Levity,MakeML,MonkeyLearn,Noogata,Obviously.ai,Pecan,RapidMiner,RunwayML,SuperAnnotate,KNIME,DashB.ai,NoCode-ML,BMW-TensorFlow-Training-GUI,Akkio
Teachable Machine-https://teachablemachine.withgoogle.com/ Vertex AI https://cloud.google.com/vertex-ai/docs/start/automl-users
Microsoft Lobe -https://lobe.ai/
Ludwig https://github.com/ludwig-ai/ludwig
WEKA - https://www.cs.waikato.ac.nz/ml/weka/ autoweka
Create ML https://developer.apple.com/documentation/createml
APAflow https://apaflow.com/?utm_medium=social&utm_source=linkedin&utm_campaign=postfity&utm_content=postfity0b527 https://apaflow.com/
Monk_Gui-https://github.com/Tessellate-Imaging/Monk_Gui
FlashML https://www.flash-ml.com/
JADBio’s https://www.jadbio.com/
JOHN SNOW LABS https://www.johnsnowlabs.com/models-training-and-active-learning-in-john-snow-labs-annotation-lab/
igel https://github.com/nidhaloff/igel
BRYTER https://bryter.com
Ushur https://ushur.com
Accern https://accern.com
Signzy https://signzy.com
Runway https://runwayml.com
Fritz AI https://www.fritz.ai
BigML, Inc https://bigml.com
MyDataModels https://lnkd.in/eejjDbM
MonkeyLearn https://monkeylearn.com
Levity https://levity.ai
Nanonets https://nanonets.com
obviously https://www.obviously.ai/
machine learning straight from Microsoft Excel https://venturebeat.com/2020/12/30/you-dont-code-do-machine-learning-straight-from-microsoft-excel/
ENNUI-https://math.mit.edu/ennui/ https://github.com/martinjm97/ENNUI https://www.youtube.com/watch?v=4VRC5k0Qs2w
Knime https://www.knime.com/
Accord.net http://accord-framework.net/
DeepDev https://realmichaelye.github.io/DeepDev/deepdev.tech%20-%20Landing%20Page/ https://github.com/realmichaelye/DeepDev
H2O Driverless AI https://www.h2o.ai/products/h2o-driverless-ai/
Oracle AutoML https://medium.com/nerd-for-tech/oracles-automl-what-it-is-and-how-it-works-12e09a832c2 https://docs.oracle.com/en-us/iaas/tools/ads-sdk/latest/user_guide/overview/overview.html
Rapid Miner https://rapidminer.com/
opennn https://www.opennn.net/
datarobot https://www.datarobot.com/
dataiku https://www.dataiku.com/product/get-started/
orange https://orange.biolab.si/
Databricks AutoML Automate Machine Learning using Databricks AutoML https://pub.towardsai.net/automate-machine-learning-using-databricks-automl-a-glass-box-approach-and-mlflow-2543a8143687
OpenBlender https://openblender.io/#/welcome https://analyticsindiamag.com/how-to-use-openblender-the-leading-data-blending-tool/
create neural networks with one line of code https://github.com/PraneetNeuro/nnio.l
AWS SageMaker AutoPilot https://aws.amazon.com/sagemaker/autopilot/
Machine Learning in JUST ONE LINE OF CODE libra https://github.com/Palashio/libra/ https://www.youtube.com/watch?v=N_T_ljj5vc4
64.tensorflow development-https://blog.tensorflow.org/
TensorFlow Hub (trained ready-to-deploy machine learning models in one place) - https://tfhub.dev/
CrypTFlow: An End-to-end System for Secure TensorFlow Inference https://github.com/mpc-msri/EzPC https://pratik-bhatu.medium.com/privacy-preserving-machine-learning-for-healthcare-using-cryptflow-cc6c379fbab7
TensorBoard.dev - https://tensorboard.dev/
tutorials-https://www.tensorflow.org/tutorials https://www.tensorflow.org/guide
TensorFlow Graphics - https://www.tensorflow.org/graphics Lattice-https://www.tensorflow.org/lattice
TensorFlow Probability-https://www.tensorflow.org/probability TensorFlow Privacy- tensorflow-privacy
https://developers.google.com/learn/topics/on-device-ml https://www.tensorflow.org/lite/guide/model_maker https://tfhub.dev/ https://www.tensorflow.org/cloud
63.Data Science in the Cloud-Amazon SageMaker,Amazon Lex,Amazon Rekognition,Azure Machine Learning (Azure ML) Services,Azure Service Bot framework,Google Cloud AutoML
64.platforms to build and deploy ML models -Uber has Michelangelo,Google has TFX,Databricks has MLFlow,Amazon Web Services (AWS) has Sagemaker
66.ML from scratch-https://dafriedman97.github.io/mlbook/content/introduction.html
https://aihubprojects.com/machine-learning-from-scratch-python/
https://github.com/python-engineer/MLfromscratch https://www.youtube.com/watch?v=rLOyrWV8gmA
67.turn-on visual training for most popular ML algorithms https://github.com/lucko515/ml_tutor https://pypi.org/project/ml-tutor/
68.mlcourse.ai is a free online- https://mlcourse.ai/
72.R for Data Science-https://r4ds.had.co.nz/ ,Fundamentals of Data Visualization-https://clauswilke.com/dataviz/
74.machine learning in JavaScript-https://www.tensorflow.org/js https://www.tensorflow.org/js/models https://tensorflow-js-object-detection.glitch.me/
TensorFlow.jl Julia with TensorFlow https://malmaud.github.io/tfdocs/ https://malmaud.github.io/TensorFlow.jl/latest/tutorial.html
Sonnet is a library built on top of TensorFlow 2 https://github.com/deepmind/sonnet
TensorFlow Federated (TFF) ( facilitate open research and experimentation with Federated Learning)-https://www.tensorflow.org/federated
TFX is an end-to-end platform for deploying production ML pipelines https://www.tensorflow.org/tfx https://github.com/tensorflow/tfx https://analyticsindiamag.com/guide-to-tensorflow-extendedtfx-end-to-end-platform-for-deploying-production-ml-pipelines/
Federated Learning -https://www.tensorflow.org/federated/tutorials/federated_learning_for_image_classification
Neural Structured Learning-https://www.tensorflow.org/neural_structured_learning/tutorials/graph_keras_mlp_cora
Responsible AI-https://www.tensorflow.org/resources/responsible-ai
https://www.tensorflow.org/graphics
75.free list of AI/ Machine Learning Resources/Courses-https://www.marktechpost.com/free-resources/
https://github.com/kabartay/OpenUnivCourses
Open ML University https://curriculum.openmlu.com/
https://www.kdnuggets.com/2018/11/10-free-must-see-courses-machine-learning-data-science.html
https://www.kdnuggets.com/2018/12/10-more-free-must-see-courses-machine-learning-data-science.html
https://www.theinsaneapp.com/2020/12/machine-learning-and-data-science-cheat-sheets-pdf.html
https://www.theinsaneapp.com/2020/11/free-machine-learning-data-science-and-python-books.html
65 Machine Learning and Data books for free- https://towardsdatascience.com/springer-has-released-65-machine-learning-and-data-books-for-free-961f8181f189
https://www.deeplearningbook.org/ http://d2l.ai/ https://www.theinsaneapp.com/2020/12/download-free-machine-learning-books.html
https://www.datasciencecentral.com/profiles/blogs/free-500-page-book-on-applications-of-deep-neural-networks-1 https://github.com/jeffheaton/t81_558_deep_learning
https://www.theinsaneapp.com/2020/12/free-data-science-books-pdf.html
https://github.com/chaconnewu/free-data-science-books
https://www.kdnuggets.com/2020/03/24-best-free-books-understand-machine-learning.html
https://www.kdnuggets.com/2020/12/15-free-data-science-machine-learning-statistics-ebooks-2021.html
http://introtodeeplearning.com/
https://www.theinsaneapp.com/2020/12/free-data-science-books-pdf.html
http://d2l.ai/index.html https://www.kdnuggets.com/2020/09/best-free-data-science-ebooks-2020-update.html
https://www.youtube.com/playlist?app=desktop&list=PLypiXJdtIca5ElZMWHl4HMeyle2AzUgVB https://mit6874.github.io/
79.For practice -https://www.confetti.ai/exams
81.Mathematics of Machine Learning,deep learning-https://towardsdatascience.com/the-mathematics-of-machine-learning-894f046c568
https://github.com/hrnbot/Basic-Mathematics-for-Machine-Learning
https://towardsdatascience.com/the-roadmap-of-mathematics-for-deep-learning-357b3db8569b
https://www.kdnuggets.com/2020/02/free-mathematics-courses-data-science-machine-learning.html
https://towardsai.net/p/data-science/how-much-math-do-i-need-in-data-science-d05d83f8cb19
https://www.mltut.com/how-to-learn-math-for-machine-learning-step-by-step-guide/
https://stanford.edu/~shervine/teaching/cs-229/cheatsheet-machine-learning-tips-and-tricks#
https://www.datasciencecentral.com/profiles/blogs/free-online-book-machine-learning-from-scratch
https://www.youtube.com/playlist?list=PLRDl2inPrWQW1QSWhBU0ki-jq_uElkh2a https://github.com/jonkrohn/ML-foundations
https://ocw.mit.edu/resources/res-18-001-calculus-online-textbook-spring-2005/textbook/
82.Googleai-https://ai.google/education
83.ELI5 is a Python package which helps to debug machine learning classifiers and explain their predictions
PyBrain is a modular Machine Learning Library for Python
84.Best Online Courses for Machine Learning and Data Science-https://www.mltut.com/best-online-courses-for-machine-learning-and-data-science/
Comprehensive Project Based Data Science Curriculum https://julienbeaulieu.github.io/2019/09/25/comprehensive-project-based-data-science-curriculum/
AI Expert Roadmap-https://i.am.ai/roadmap/#data-science-roadmap
86.Yann LeCun’s Deep Learning Course at CDS-https://cds.nyu.edu/deep-learning/ https://atcold.github.io/pytorch-Deep-Learning/
https://atcold.github.io/pytorch-Deep-Learning/
https://www.cs.cmu.edu/~ninamf/courses/601sp15/lectures.shtml
88.Python Data Science Handbook https://jakevdp.github.io/PythonDataScienceHandbook/
91.AudioFeaturizer when deal with audio data- https://pypi.org/project/AudioFeaturizer/
liborsa library https://librosa.org/doc/latest/index.html
MAGENTA-https://magenta.tensorflow.org/
pydub https://github.com/jiaaro/pydub
DDSP: Differentiable Digital Signal Processing https://github.com/magenta/ddsp https://analyticsindiamag.com/guide-to-differentiable-digital-signal-processing-ddsp-library-with-python-code/
92.Palladium-https://palladium.readthedocs.io/en/latest/
94.Facebook Open Sourced New Frameworks to Advance Deep Learning Research https://www.kdnuggets.com/2020/11/facebook-open-source-frameworks-advance-deep-learning-research.html
95.Software Engineering for Machine Learning https://github.com/SE-ML/awesome-seml
96.Atlas web-based dashboard -https://www.atlas.dessa.com/
97.Pytest (test code) https://docs.pytest.org/en/latest/index.html (test code)
98.keras- https://keras.io/ https://keras.io/api/ https://keras.io/examples/
99.High-Performance Jupyter Notebook - BlazingSQL Notebooks https://blazingsql.com/notebooks
jupyter-tabnine https://github.com/wenmin-wu/jupyter-tabnine
101.Kubeflow Machine Learning Toolkit for Kubernetes https://www.kubeflow.org/
102.Daily AI updates to your inbox- https://sago-ai.news/#/
103.Three API styles - Sequential Model,functional API,Model subclassing
104.Deep Learning Toolkit for Medical Image Analysis -https://github.com/DLTK/DLTK
3 Python Packages for Machine Learning Validation Evidently,Deepchecks,TensorFlow-Data-Validation
106.Explainability : Model-Specific explainability(Explainability method is strictly relevant to specific model) ,Model-Agnostic explainability ( Explanation to any type model),Model-Centric explainability(most Explanation methods are Model-Centric, as these methods are used to explain how the features and target values are being adjusted),Data-Centric explainability(these methods are used to understand the nature of the data)
Interpret The ML Model https://towardsdatascience.com/explainable-artificial-intelligence-part-3-hands-on-machine-learning-model-interpretation-e8ebe5afc608
https://christophm.github.io/interpretable-ml-book/ https://www.kaggle.com/getting-started/209632 https://ex.pegg.io/
shap,lime,Shapash,webshap,ELI5,InterpretML,Concept Relevance Propagation,OmniXAI,Treeinterpreter,Dalex,Eli5,Yellowbrick,Mlxtend,PDPBox,InterpretML,Partial Dependence Plots (PDP), Individual Conditional Expectation (ICE) Plots, Accumulated Local Effects (ALE) Curves and Permutation Importance,Casual shap values,Integrated Gradients,Anchors,Feature importance/attribution,SmoothGrad,DeepLIFT,GradientExplainer,decision tree surrogates,Permutation feature importance, xplique,ANCHORS,Permutation Importance,Morris Sensitivity Analysis,Contrastive Explanation Method (CEM),Counterfactual Instances,Global Interpretation via Recursive,Partitioning (GIRP),Protodash,Scalable Bayesian Rule Lists,Tree Surrogates,Explainable Boosting Machine (EBM),DALEX,ALIBI,DiCE,Explainerdashboards,TCAV,PiML,Xplique,Explainer_dashboard,InterpretML,tcav,FeatureImportance,Layerwise Propagation,Surrogate,Explainer Partial Dependence,solas,ferret,Integrated Gradients,DeepLift,Explainable Boosting Machine,Saliency maps,TCAV,Distillation,Counterfactual,interpretML,pdpbox,PyALE,interpret, Fast interpretable,greedy-tree sums,interpretml,imodels,ferret,Counterfactual explanations ,Layerwise Relevance Propagation,Integrated Gradients (IG),Deep LIFT, Saliency,Feature Ablation,Occlusion,captum,Accumulated Local Effects,Anchors,Integrated Gradients,Counterfactuals,GradientShap,FastTreeShap,DeepLift,DeepLiftShap,IntegratedGradients,LayerConductance,NeuronConductance,NoiseTunnel,InterpretML,ALIBI DiCE,interpret-text,aix360,OmniXAI,BreakDown,interpret-text,iml (Interpretable Machine Learning),OmniXAI,Explainerdashboard,InterpretML,ELI5,Netron,DoWhy,CausalNex,explainerdashboard,fairlearn,arviz,Explainability,iNNvestigate,Model Analysis,Permutation feature importance,Partial dependency plots,TE2Rules
OmniXAI: A Library for eXplainable AI https://github.com/salesforce/OmniXAI
Xplique is a Neural Networks Explainability Toolbox https://github.com/deel-ai/xplique/
Ethical-AI Toolkits https://murat-durmus.medium.com/an-brief-overview-of-some-ethical-ai-toolkits-712afe9f3b3a
ferret python package for benchmarking interpretability techniques https://github.com/g8a9/ferret
explaining machine learning models https://github.com/SeldonIO/alibi https://github.com/salesforce/OmniXAI https://github.com/SeldonIO/alibi
Awesome-explainable-AI https://ex.pegg.io/
tf-explain https://github.com/sicara/tf-explain imodels https://github.com/csinva/imodels
lime(explain black box models)- https://lime-ml.readthedocs.io/en/latest/ https://towardsdatascience.com/interpreting-image-classification-model-with-lime-1e7064a2f2e5
SHAP and Kernel SHAP,TreeSHAP,shparkley,Shparkley,Deep SHAP,TimeSHAP,PySpark-SHAP,GPUTreeSHAP,FastTreeSHAP: Accelerating SHAP value computation for trees https://github.com/linkedin/fasttreeshap
https://github.com/slundberg/shap https://www.kdnuggets.com/2020/01/explaining-black-box-models-ensemble-deep-learning-lime-shap.html https://analyticsindiamag.com/hands-on-guide-to-interpret-machine-learning-with-shap/
fastshap https://github.com/bgreenwell/fastshap
xplique https://github.com/deel-ai/xplique?utm_source=pocket_mylist
Shapash makes Machine Learning models transparent and understandable by everyone https://github.com/MAIF/shapash https://www.kdnuggets.com/2021/04/shapash-machine-learning-models-understandable.html
Captum is a model interpretability and understanding library for PyTorch https://github.com/pytorch/captum
Explainable AI https://ex.pegg.io/
Explainable AI dashboards https://github.com/oegedijk/explainerdashboard https://www.youtube.com/watch?v=ZgypAMRcmw8
interpret https://github.com/interpretml/interpret mlxtend's http://rasbt.github.io/mlxtend/
imodels Interpretable ML package https://github.com/csinva/imodels
Quantus eXplainable AI toolkit https://github.com/understandable-machine-intelligence-lab/quantus
DiCE Generate Diverse Counterfactual Explanations for any machine learning model. https://github.com/interpretml/DiCE
tcav https://github.com/tensorflow/tcav yellowbrick https://www.scikit-yb.org/en/latest/quickstart.html
Language Interpretability Tool https://github.com/pair-code/lit https://ai.googleblog.com/2020/11/the-language-interpretability-tool-lit.html
Transformers Interpret https://towardsdatascience.com/introducing-transformers-interpret-explainable-ai-for-transformers-890a403a9470 https://github.com/cdpierse/transformers-interpret
treeinterpreter https://github.com/andosa/treeinterpreter
Adversarial Explainable AI https://github.com/hbaniecki/adversarial-explainable-ai https://medium.com/responsibleml/adversarial-attacks-on-explainable-ai-f65d41e83c5f
Captum Model Interpretability for PyTorch https://captum.ai/ https://github.com/pytorch/captum
ecco https://github.com/jalammar/ecco https://jalammar.github.io/explaining-transformers/ https://www.eccox.io/
dalex https://pypi.org/project/dalex/ https://blog.learningdollars.com/2021/01/02/ai-in-medical-diagnosis/ https://www.kdnuggets.com/2020/11/dalex-explain-tensorflow-model.html
google AI Explanations for AI Platform https://cloud.google.com/ai-platform/prediction/docs/ai-explanations/overview?utm_source=youtube&utm_medium=Unpaidsocial&utm_campaign=guo-20200423-Intro-Aiexp
eli5 https://eli5.readthedocs.io/en/latest/
Integrated-Gradients https://github.com/ankurtaly/Integrated-Gradients
xplique https://github.com/deel-ai/xplique/
TabNet: Attentive Interpretable Tabular Learning https://github.com/dreamquark-ai/tabnet
skater https://oracle.github.io/Skater/
lucid https://github.com/tensorflow/lucid/ https://www.kdnuggets.com/2020/04/openai-open-sources-microscope-lucid-library-neural-networks.html
what if tool https://pair-code.github.io/what-if-tool/ https://pair-code.github.io/what-if-tool/demos/uci.html
themis https://themis-ml.readthedocs.io/en/latest/
DeepLIFT https://github.com/kundajelab/deeplift
explainerdashboard https://towardsdatascience.com/the-quickest-way-to-build-dashboards-for-machine-learning-models-ec769825070d
Responsible AI-https://www.tensorflow.org/resources/responsible-ai
fairlearn https://github.com/fairlearn/fairlearn fairml https://github.com/adebayoj/fairml https://www.datasciencecentral.com/profiles/blogs/fairml-auditing-black-box-predictive-models
fair https://medium.com/responsibleml/how-to-easily-check-if-your-ml-model-is-fair-2c173419ae4c
cleverhans https://github.com/cleverhans-lab/cleverhans
Google Facets https://pair-code.github.io/facets/
Google’s Model Card Toolkit
Opening the AI Black Box -https://zetane.com/gallery
Rulex Explainable AI https://www.rulex.ai/rulex-explainable-ai-xai/
AI Explainability 360 Toolkit from IBM Research https://aix360.mybluemix.net/ https://analyticsindiamag.com/guide-to-ai-explainability-360-an-open-source-toolkit-by-ibm/
onnx https://github.com/onnx/onnx
torch-dreams https://github.com/Mayukhdeb/torch-dreams
https://github.com/jphall663/awesome-machine-learning-interpretability
https://christophm.github.io/interpretable-ml-book/ https://github.com/christophM/interpretable-ml-book
https://www.kdnuggets.com/2018/12/machine-learning-explainability-interpretability-ai.html https://www.kdnuggets.com/2019/09/python-libraries-interpretable-machine-learning.html https://www.kdnuggets.com/2019/08/open-black-boxes-explainable-machine-learning.html
Fairness https://analyticsindiamag.com/building-a-responsible-ai-eco-system/
How to easily check if your Machine Learning model is fair (dalex) https://www.kdnuggets.com/2020/12/machine-learning-model-fair.html
TensorFlow Federated,TensorFlow Model Remediation,TensorFlow Privacy,LinkedIn Fairness Toolkit,Fairlearn,AI Fairness 360,Responsible AI Toolbox,XAI,scikit-fairness,Fairlead,Algofairness,Aequitas,CERTIFAI,ML-fairness-gym,Algofairness,FairSight,GD-IQ,scikit-fairness,Mitigating Gender Bias In Captioning System,Model Card Toolkit,AI Fairness 360, AI Explainability 360, Adversarial Robustness 360, Uncertainty Quantification 360, AI Privacy 360, Causal Inference 360, and AI FactSheets 360,Deon,Responsible AI Toolbox,DALEX,TensorFlow Data Validation,XAI,Fawkes,AdverTorch,solasai,Fawkes,Gluru,AdverTorch,Conversica,Quill AI,Fairness 360,Fairlead, TextAttack,Themis-ML,Debiaswe,fairness-in-ml,bias-correction,BlackBoxAuditing,fairness-indicators,Awesome-Fairness-in-AI
107.deep-learning-drizzle -https://deep-learning-drizzle.github.io/
108.Machine Learning University - https://aws.amazon.com/machine-learning/mlu/
109.Continuous Machine Learning (CML),OpenMLOps,Metaflow,Kubeflow,Data Version Control (DVC),Kedro
mlflow https://mlflow.org/ An open source platform for the machine learning lifecycle
Layer https://docs.app.layer.ai/docs/
https://www.kdnuggets.com/2021/01/5-tools-effortless-data-science.html
https://azure.microsoft.com/en-us/services/machine-learning/
https://github.com/VertaAI/modeldb
110.Data Preparation / ETL https://airflow.apache.org/ https://intake.readthedocs.io/en/latest/
111.fairlearn https://github.com/fairlearn/fairlearn/blob/master/README.md Evaluating fairness of AI/ML models and training data and for mitigating bias in models determined to be unfair.
AI Fairness 360 evaluating fairness of AI/ML models and training data and mitigating bias in current models https://aif360.mybluemix.net/
An ethics checklist for data scientists https://deon.drivendata.org/
112.https://analyticsindiamag.com/top-6-ai-powered-drug-discovery-tools-in-2021/
MONAI Framework For Medical Imaging Research https://analyticsindiamag.com/monai-datatsets-managers/
torchio https://github.com/fepegar/torchio https://analyticsindiamag.com/torchio-3d-medical-imaging/
MolBert: Molecular Representation learning with AI
medicalAI https://github.com/aibharata/medicalAI
Biopython is a set of freely available tools https://github.com/biopython/biopython
DeepIPW https://github.com/ruoqi-liu/DeepIPW
113.OpenVINO https://opencv.org/openvino-model-optimization/ https://opencv.org/how-to-speed-up-deep-learning-inference-using-openvino-toolkit-2/
114.https://neptune.ai/blog/machine-learning-model-management https://analyticsindiamag.com/top-mlops-tools-github-repos/ https://neu.ro/2021-mlops-platforms-vendor-analysis-report/
Best Workflow and Pipeline Orchestration Tools https://neptune.ai/blog/best-workflow-and-pipeline-orchestration-tools?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-best-workflow-and-pipeline-orchestration-tools
MLflow vs Kubeflow vs Neptune https://neptune.ai/blog/mlflow-vs-kubeflow-vs-neptune-differences?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-mlflow-vs-kubeflow-vs-neptune-differences
15 MLOps.toys https://mlops.toys/ AIOps,Data version control DVC,MLFlow,Docker foundation,Kubernetes Foundation,Tensorflow Extend (TFX),Kubeflow,AWS AIOps,Azure AIOps,MLflow and TensorBoard ,Weights & Biases, Neptune AI, Comet,aim
Data verification:Scale Nucleus,great_expectation,Soda Data Observability
Metadata management:Neptune.ai,SiaSearch,Tensorflow's ML MetaData
Data management:Neptune,DVC,RoboFlow,Dataiku,Apache Airflow, Apache NiFi, Apache Kafka
Feature Stores : Amazon SageMaker Feature Store,Databricks,Hopsworks.ai,Vertex AI,FeatureForm,FeastTecton,butterfree,ByteHub
Data Quality:whylogs,eurybia
Detecting data drift and model drift:eurybia
Experiment tracking :Kedro,modeldb,mlflow,DVC,weight and biases,Neptune,clearly,tensorboard,determined,polyaxon,mlrun,Comet,Sacred,TensorBoard,DagsHub,Guild AI,ClearML,Valohai,Pachyderm,Verta.ai,Kubeflow,SageMaker Studio,sacred
Monitoring: Prometheus, Grafana, ELK Stack
Data versioning:Dolt,DVC,gitlfs,pachyderm, Git LFS,lakefs,DVC,weight and biases,Neptune,Comet,Delta Lake
Data Governance: Collibra, Alation, Informatica
Data Quality: Trifacta, Talend, Informatica
Code versioning: Gitlab,github,SVN
Model Versioning :Neptune,ModelDB,DVC,MLFlow,Pachyderm,Polyaxon
Pipeline orchestration:Kale,Apche airflow,Argo,workflows,Luigi,kubeflow,kedro,nextflow,dragster,Apache,bean,zenml,flute,prefect,ray,DVC,polyaxon,clearml,mlrun,pachyderm,Metaflow,Couler,Valohai,Dagster.io
Runtime engine:Ray,nuclio,dask,horovod,Apache,spark
Data orchestration prefect,kale,mlru,dagster,kedro,airflow
Artifact tracking:Kubeflow,mlflow,weight and biases,Neptune,polyaxon,clearml,mlrun,pachyderm
Model registry:Modeldb,mlflow,determined,weight and biases,Neptune,clearml,mlrun, Vision AI,DINO,Amazon Rekognition
Model serving:Seldon,core,bentoml,tensorflow serving,kserve,fastapi,torchserve,ray,mlflow,clearml,mlrun,pymlpipe,TorchServe,TensorFlow Serving,Kubeflow,Cortex,Seldon.ai,ForestFlow,bentoml
Model monitoring:Evidently,WhyLabs,grafana,alibi,detect,modeldb,clearml,mlrun,prometheus,pymlpipe,NannyML,Aporia,eurybia,Arize,Fiddler,Amazon SageMaker Model Monitor,Prometheus,Qualdo,Neptune,Grafana + Prometheus ,Qualdo,Seldon Core,Censius
Model Performance Tracking: TensorBoard, MLflow, Comet.ml
Continuous Integration: Jenkins, Travis CI, CircleCI
Continuous Deployment: Jenkins, Travis CI, CircleCI
Containerization: Docker, Kubernetes
Configuration Management: Ansible, Puppet, Chef
data validation:Pydantic,eurybia
model testing: Deepchecks,Neptune,Mona ,Grafana + Prometheus
Model Security: Seldon, OpenVino, TensorFlow Privacy
Continuous Integration and Continuous Deployment (CI/CD) Tools for Machine Learning : CML ,GitHub Actions,GitLab for CI/CD,Jenkins,TeamCity,Circle CI,Travis CI,
aim https://github.com/aimhubio/aim
Metaflow,MLReef,MLRun,ZenML,MLflow,Seldon,Bodywork,Pachyderm,DVC, or Data Version Control
MLOps https://analyticsindiamag.com/8-projects-to-kickstart-your-mlops-journey-in-2021/
Open MLOps https://github.com/datarevenue-berlin/OpenMLOps
Best Tools for Tracking Machine Learning Experiments https://neptune.ai/blog/best-ml-experiment-tracking-tools
mlops-https://github.com/visenger/awesome-mlops
mlflow https://towardsdatascience.com/get-started-with-mlops-fd7062cab018
GuildAI https://guild.ai/ https://github.com/guildai/guildai
MLOPS https://www.analyticsinsight.net/top-mlops-based-tools-for-enabling-effective-machine-learning-lifecycle/ https://neptune.ai/blog/best-mlops-tools
ML-Model-CI https://github.com/cap-ntu/ML-Model-CI
Easy MLOps with PyCaret + MLflow https://www.kdnuggets.com/2021/05/easy-mlops-pycaret-mlflow.html
https://www.kdnuggets.com/2021/03/overview-mlops.html https://medium.com/prosus-ai-tech-blog/towards-mlops-technical-capabilities-of-a-machine-learning-platform-61f504e3e281
omegaml https://github.com/omegaml/omegaml
https://mlops.githubapp.com/ https://about.mlreef.com/blog/global-mlops-and-ml-tools-landscape https://github.com/paiml/practical-mlops-book
https://docs.microsoft.com/en-us/azure/architecture/example-scenario/mlops/mlops-technical-paper https://neptune.ai/blog/end-to-end-mlops-platforms
https://github.com/kelvins/awesome-mlops#hyperparameter-tuning
ClearML https://analyticsindiamag.com/guide-to-clearml-zero-integration-mlops-solution/
https://ml-ops.org/content/mlops-principles
Monitoring: Evidently https://evidentlyai.com/ , Seldon Alibi https://github.com/SeldonIO/alibi-detect
115.Code faster https://www.tabnine.com/
117.https://www.pye.ai/2021/03/19/machine-learning-model-management-what-why-and-how/ https://www.ambiata.com/blog/2020-12-07-mlops-tools/
Pachyderm Kubeflow MLflow Metaflow ZenML Seldon Bodywork MLReef MLRun DVC katana-skipper Weights & Biases Valohai Polyaxon Neptune.ai CometML Algorithmia clearml, airflow, kedro, GitHub Actions Flyte Valohai Seldon Iguazio Datarobot Dataiku cnvrg.io ClearML AWS Sagemaker wandb evidently
BentoML Unified Model Serving Framework https://github.com/bentoml/BentoML
mlflow https://mlflow.org/docs/latest/index.html https://github.com/amesar/mlflow-examples
MLFlow by pycaret https://pycaret.org/mlflow/?utm_medium=social&utm_source=linkedin&utm_campaign=postfity&utm_content=postfity2c1c2
labml https://ramith.fyi/tracking-your-ml-experiments-without-sending-data-to-the-cloud/
MLOps https://github.com/microsoft/MLOps https://mlops.githubapp.com/ https://huyenchip.com/2020/12/30/mlops-v2.html https://github.com/paiml/practical-mlops-book https://analyticsindiamag.com/top-10-tools-to-kickstart-your-mlops-journey-in-2021
mlops platform SageMaker on Amazon,Data Lab,Domino,H2O MLOps,Cloudera,Data Platform,Kubeflow,MLFlow,Metaflow,Flyte,ZenML,MLRun,Algorithmia,Dataiku,DataRobot,Pachyderm,Databricks,Lakehouse,Neptune.ai
7 Best Resources To Learn MLOps In 2021 https://analyticsindiamag.com/7-best-resources-to-learn-mlops-in-2021/
DevOps https://github.com/collections/devops-tools
airflow https://github.com/apache/airflow
kubeflow https://github.com/kubeflow/kubeflow
kubernetes https://github.com/kubernetes/kubernetes
Metaflow https://metaflow.org/ https://github.com/Netflix/metaflow
pipeline https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html
Tensorflow Extended https://www.tensorflow.org/tfx Tensorflow Transform https://www.tensorflow.org/tfx/transform/get_started
Serving Models https://www.tensorflow.org/tfx/guide/serving
Tensorflow Data Validation https://www.tensorflow.org/tfx/data_validation/get_started TensorFlow Model Analysis https://www.tensorflow.org/tfx/model_analysis/get_started
Model Validation Toolkit https://finraos.github.io/model-validation-toolkit/ https://github.com/FINRAOS/model-validation-toolkit
MLflow Open-source platform for tracking machine learning experiments https://mlflow.org/ https://analyticsindiamag.com/guide-to-mlflow-a-platform-to-manage-machine-learning-lifecycle/ https://www.kdnuggets.com/2021/01/model-experiments-tracking-registration-mlflow-databricks.html
ray https://docs.ray.io/en/master/serve/ https://github.com/ray-project/ray
Top 10 Leading Machine Learning Feature Stores https://www.pye.ai/2021/05/14/top-10-machine-learning-feature-store-systems/
118.algorithm to use by problem https://www.datasciencecentral.com/profiles/blogs/which-machine-learning-deep-learning-algorithm-to-use-by-problem
119.Connect the world to your data and fuel your ML.
OpenBlender Enrich ML Models with adding new Variables from Any Source to Boost Performance https://www.youtube.com/channel/UCCFN8DDrA6k7eHYLvZGdNVA https://openblender.io/
- Google's MuRIL (Multilingual Representations for Indian Languages) https://tfhub.dev/google/MuRIL/1
122.tools-https://towardsdatascience.com/data-science-tools-f16ecd91c95d
123.Elements of AI free online course https://www.elementsofai.com/
124.Best_AI_paper_2020 https://github.com/louisfb01/Best_AI_paper_2020
125.roadmap https://github.com/graykode/nlp-roadmap https://www.theinsaneapp.com/2021/03/roadmap-series.html
https://www.freecodecamp.org/news/data-science-learning-roadmap/ https://www.kdnuggets.com/2020/12/roadmaps-ai-developer-data-scientist-machine-learning-engineer.html
https://github.com/AMAI-GmbH/AI-Expert-Roadmap
data-engineer-roadmap https://github.com/datastacktv/data-engineer-roadmap
Visualizing the Execution of Python Program http://pythontutor.com/ https://www.youtube.com/watch?v=pCSlWQjfCzA
MLPerf Model performance debugging tools https://mlperf.org/
Model debugging tools Manifold https://eng.uber.com/manifold/
Pytest for Data Scientists https://towardsdatascience.com/4-lessor-known-yet-awesome-tips-for-pytest-2117d8a62d9c
Icecream https://towardsdatascience.com/stop-using-print-to-debug-in-python-use-icecream-instead-79e17b963fcc
Experiment tracking tools WandB https://wandb.ai/site
Comet manage and organize machine learning experiments https://www.comet.ml/site/ https://analyticsindiamag.com/how-to-supercharge-your-machine-learning-experiments-with-comet-ml/
neptune https://neptune.ai/ https://analyticsindiamag.com/how-to-manage-ml-experiments-with-neptune-ai/
weights & biases https://wandb.ai/site https://analyticsindiamag.com/hands-on-guide-to-weights-and-biases-wandb-with-python-implementation/ https://docs.wandb.ai/
https://www.kdnuggets.com/2020/07/tour-end-to-end-machine-learning-platforms.html
127.19 Best JupyterLab Extensions for Machine Learning https://neptune.ai/blog/jupyterlab-extensions-for-machine-learning
128.coreml https://developer.apple.com/machine-learning/core-ml/
129.Protect Your Neural Networks Against Hacking Adversarial Robustness Toolbox (ART) https://analyticsindiamag.com/adversarial-robustness-toolbox-art/
131.datascience-fails https://github.com/xLaszlo/datascience-fails
132.Jupyter notebook integration for Microsoft Excel https://github.com/pyxll/pyxll-jupyter https://towardsdatascience.com/python-jupyter-notebooks-in-excel-5ab34fc6439
Voilà turns Jupyter notebooks into standalone web applications https://github.com/voila-dashboards/voila https://github.com/voila-dashboards/voila-gridstack
How to Optimize Your Jupyter Notebook https://www.kdnuggets.com/2020/01/optimize-jupyter-notebook.html
TabNet: Attentive Interpretable Tabular Learning https://github.com/dreamquark-ai/tabnet
133.rapidly develop data applications with Python https://github.com/dstackai/dstack
134.Google Research: Looking Back at 2020, and Forward to 2021 https://ai.googleblog.com/2021/01/google-research-looking-back-at-2020.html
135.cortex Run inference at scale https://www.cortex.dev/ https://github.com/cortexlabs/cortex
136.https://www.theinsaneapp.com/2020/12/machine-learning-and-data-science-cheat-sheets-pdf.html
137.Federated Learning Systems
Flower – A Framework To Build Federated Learning Systems https://github.com/adap/flower https://flower.dev/
138.https://analyticsindiamag.com/top-ai-powered-writing-assistants-to-create-better-content/
139.Tensorflow Data Validation - Data Analysis At Scale https://www.youtube.com/watch?v=eGIG_qHgQ08
140.SciKeras https://scikeras.readthedocs.io/en/latest/#
141.debugging Data viewer https://devblogs.microsoft.com/python/python-in-visual-studio-code-january-2021-release/
142.Machine Learning Lifecycle in 2021 https://towardsdatascience.com/the-machine-learning-lifecycle-in-2021-473717c633bc
143.Introduction To ML.NET – An ML Framework For DOTNET Developers https://analyticsindiamag.com/introduction-to-ml-net-a-machine-learning-framework-for-dotnet-developers/
https://analyticsindiamag.com/step-by-step-guide-for-image-classification-using-ml-net/
144.https://www.perceptilabs.com/home http://deeplearninggallery.com/ https://www.kdnuggets.com/2019/01/practical-apache-spark-10-minutes.html
145.https://www.kdnuggets.com/2018/09/machine-learning-cheat-sheets.html https://www.kdnuggets.com/2018/09/meverick-lin-data-science-cheat-sheet.html
https://www.kdnuggets.com/2018/08/data-visualization-cheatsheet.html https://www.kdnuggets.com/2018/07/sql-cheat-sheet.html https://www.kdnuggets.com/2018/04/python-regular-expressions-cheat-sheet.html https://www.kdnuggets.com/2017/09/essential-data-science-machine-learning-deep-learning-cheat-sheets.html
https://www.analyticsvidhya.com/blog/2021/01/5-python-packages-every-data-scientist-must-know/
https://www.kdnuggets.com/2021/01/ultimate-scikit-learn-machine-learning-cheatsheet.html https://www.kdnuggets.com/2020/09/10-things-know-scikit-learn.html
146.Data Pipelines https://www.kdnuggets.com/2018/05/beginners-guide-data-science-pipeline.html https://www.kdnuggets.com/2019/03/data-pipelines-luigi-airflow-everything-need-know.html
- AI Habitat: A Platform For Embodied AI Research https://analyticsindiamag.com/hands-on-guide-to-ai-habitat-a-platform-for-embodied-ai-research/
152.Best ML Frameworks & Extensions for Scikit-learn https://neptune.ai/blog/the-best-ml-framework-extensions-for-scikit-learn?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-the-best-ml-framework-extensions-for-scikit-learn
153.Multimodal Neurons, The Most Advanced Neural Networks Discovered By OpenAI https://analyticsindiamag.com/inside-multimodal-neurons-the-most-advanced-neural-networks-discovered-by-openai/
154.TensorGram https://github.com/ksdkamesh99/TensorGram https://www.youtube.com/watch?v=ItDBQB4YFuI
knockknock https://towardsdatascience.com/how-to-get-notified-when-your-model-is-done-training-with-knockknock-483a0475f82c
labmi Organize machine learning experiments and monitor training progress from mobile https://labml.ai/
WeightWatcher https://github.com/CalculatedContent/WeightWatcher
labml Monitor deep learning model training and hardware usage from your mobile phone https://labml.ai/ https://github.com/labmlai/labml
ml notify https://github.com/aporia-ai/mlnotify
155.r packages https://upurl.me/vkf3r http://r-bloggers.com/2021/04/15-essential-packages-in-r-for-data-science/ https://www.ubuntupit.com/best-r-machine-learning-packages/
Top 10 Free Resources To Learn R https://analyticsindiamag.com/top-10-free-resources-to-learn-r/
analyticsvidhya.com/blog/2021/04/top-10-r-packages-for-data-science-you-must-know-in-2021/
156.Top Julia Libraries for Machine Learning https://www.analyticsvidhya.com/blog/2021/05/top-julia-machine-learning-libraries/
156.openblender Fuel your ML Engines with Relevant Data to Boost Performance https://openblender.io/#/welcome
157.all Domain-based A.I. Platform for Data Scientists https://www.cluzters.ai/
158.2D images to 3D https://analyticsindiamag.com/python-guide-to-neural-body-converting-2d-images-to-3d/
Open3D: An Open Source Modern Library For 3D Data Processing https://github.com/intel-isl/Open3D
160.https://gallery.allennlp.org/ https://prior.allenai.org/projects/gpv
161.NVIDIA Unveils 50+ New, Updated AI Tools and Trainings for Developers https://www.hpcwire.com/off-the-wire/nvidia-unveils-50-new-updated-ai-tools-and-trainings-for-developers/
162.Best Workflow and Pipeline Orchestration Tools https://neptune.ai/blog/best-workflow-and-pipeline-orchestration-tools?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-best-workflow-and-pipeline-orchestration-tools
164.notes Data Science & Machine Learning https://chrisalbon.com/
165.black uncompromising Python code formatter https://github.com/psf/black
166.Feature stores https://www.kdnuggets.com/2021/05/feature-stores-how-avoid-feeling-every-day-is-groundhog-day.html https://neptune.ai/blog/feature-stores-components-of-a-data-science-factory-guide?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-feature-stores-components-of-a-data-science-factory-guide
167.Code and Notebook Versioning for ML Teams https://neptune.ai/blog/code-and-notebook-versioning-for-ml-teams-guide?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-code-and-notebook-versioning-for-ml-teams-guide
10 tools that can serve as a great alternative to the different parts of ClearML https://neptune.ai/blog/clear-ml-alternatives?utm_source=twitter&utm_medium=tweet&utm_campaign=blog-clear-ml-alternatives
168.3 Tools to Track and Visualize the Execution of your Python Code https://towardsdatascience.com/3-tools-to-track-and-visualize-the-execution-of-your-python-code-666a153e435e
Follow leaders in the field to update yourself in the field
1.Linkedin
2.Twitter
CPU/GPU/TPU
1.Google cloab (FREE) Jupyter Lab for Python, R, Swift from Google Colab with ColabCode https://www.youtube.com/watch?v=Q35WIqZoUF4
https://www.analyticsvidhya.com/blog/2021/01/avid-user-of-google-colab-here-are-some-alternatives-of-google-colab-that-you-should-know-about/?utm_source=linkedin&utm_medium=social&utm_campaign=old-blog&utm_content=B&custom=FBI156
https://towardsdatascience.com/use-colab-more-efficiently-with-these-hacks-fc89ef1162d8 https://www.analyticsvidhya.com/blog/2021/05/10-colab-tips-and-hacks-for-efficient-use-of-it/
ColabCode This is an amazing extension to the already available resource, Google Colab https://github.com/abhi1thakur/colabcode
GitHub notebooks with Google Colab https://www.youtube.com/watch?v=LmIylxNmA-A&feature=youtu.be
colab_everything Python library to run streamlit, flask, fastapi, etc on google colab https://github.com/Ankur-singh/colab_everything/
2.Kaggle kernel(read terms and conditions before use) (FREE)
3.Paperspace Gradient(read terms and conditions before use)
4.knime - https://www.knime.com/(read terms and conditions before use)
5.RapidMiner (read terms and conditions before use)
https://github.com/zszazi/Deep-learning-in-cloud
6.saturncloud https://saturncloud.io/
Intel Jupyter Lab,Amazon Sagemaker,Binder,DeepNote,Hex,DataBricks Notebook,Jetbrains Datalore,DataCamp Workspace,Notablejournal,Notable,Observable,CoCalc,Replit,Binder,IBM DataPlatform Notebooks,CodeSandbox,StackBlitz
So what next ?
participate online competition and do project and apply to intership ,job,solving real world problems, etc...
applications of data science in many industry
1.E-commerce- Identifying consumers,Recommending Products,Analyzing Reviews
2.Manufacturing- Predicting potential problems,Monitoring systems,Automating manufacturing units, Maintenance Scheduling,Anomaly Detection
3.Banking- Fraud detection,Credit risk modeling,Customer lifetime value
4.Healthcare- Medical image analysis, Drug discovery,Bioinformatics,Virtual Assistants,image segmentation
5.Transport- Self-driving cars,Enhanced driving experience,Car monitoring system,Enhancing the safety of passengers
6.Finance- Customer segmentation,Strategic decision making,Algorithmic trading,Risk analytics
7.Marketing (Added from comments Credits: Jawad Ali)- LTV predictions,Predictive analytics for customer behavior,Ad targeting
and many more fields - https://www.topbots.com/enterprise-ai-companies-2020/ , https://venturebeat.com/2020/10/21/the-2020-data-and-ai-landscape/
Research blogs https://www.theinsaneapp.com/2021/04/top-machine-learning-blogs-to-follow-in-2021.html
Explainpaper https://www.explainpaper.com/
https://reconshell.com/top-ai-and-machine-learning-blogs-curated-for-ai-enthusiasts/
1.https://ai.facebook.com/ https://ai.facebook.com/blog/
3.https://deepmind.com/blog https://deepai.org/definitions
5.https://www.malongtech.com/en/research.html
6.https://blogs.nvidia.com/blog/tag/artificial-intelligence/ https://blogs.nvidia.com/
https://ai.googleblog.com/2021/01/google-research-looking-back-at-2020.html?m=1
7.https://blog.tensorflow.org/
kdnuggets.com
https://www.kdnuggets.com/2020/01/top-10-ai-ml-articles-to-know.html
RESEARCH LABS IN THE WORLD
https://ai.facebook.com/ https://ai.googleblog.com/ https://research.google/ https://ai.google/research/
1.The Alan Turing Institute:https://www.turing.ac.uk/
2.J.P. Morgan AI Research Lab:https://www.jpmorgan.com/insights/tec...
3.Oxford ML Research Group:http://www.robots.ox.ac.uk/~parg/proj...
4.Microsoft Research Lab- AI:https://www.microsoft.com/en-us/resea...
5.Berkeley AI Research:https://bair.berkeley.edu/
6.LIVIA:https://en.etsmtl.ca/Unites-de-recher...
7.MIT Computer Science and Artificial :https://www.csail.mit.edu/
online competitions:
Top 25 Machine Learning Hackathons https://medium.com/analytics-vidhya/top-25-machine-learning-hackathons-its-here-now-for-anyone-to-move-to-data-science-a93deb2a198a
1.Kaggle-https://www.kaggle.com/
kaggle-solutions https://github.com/faridrashidi/kaggle-solutions
2.hackerearth-https://www.hackerearth.com/challenges/
3.machinehack-https://www.machinehack.com/
4.analyticsvidhya-https://datahack.analyticsvidhya.com/contest/all/
5.zindi-https://zindi.africa/competitions
6.crowdai-https://www.crowdai.org/
7.driven data-https://www.drivendata.org/
8.dockship-https://dockship.io/Runway AI
9.SIGNATE Competition- https://signate.jp/about?rf=competition_about
9.International Data Analysis Olympiad (IDAHO)
10.Codalab
11.Iron Viz
12.Data Science Challenges
13.Tianchi Big Data Competition
14.https://www.techgig.com/hackathon/ml_hackathon
https://towardsdatascience.com/12-data-science-ai-competitions-to-advance-your-skills-in-2021-32e3fcb95d8c https://www.kdnuggets.com/2020/09/international-alternatives-kaggle-data-science-competitions.html
Some useful content :
- H20.ai automl, google automl,Google Cloud AutoML,google ml kit(https://developers.google.com/ml-kit) ,Azure Cognitive Services,Azure Machine Learning Service,amazon ml,Azure Machine Learning Studio,Google Cloud Platform,gcp automl ision,Weka,AutoWeka,Microsoft Cognitive Toolkit,Google Cloud AutoML,DataRobot AutoML,Databricks AutoML,Azure ML,azure machine learning studio,IBM Watson ml studio,AWS Sagemaker Studio,aws rekognition,Google AI Platform,Databricks,Domino Data Lab,roboflow,Qlik AutoML,NVIDIA TAO
H2O Driverless AI https://www.h2o.ai/products/h2o-driverless-ai/
H2O Flow - Web Based Machine Learning Development https://docs.h2o.ai/h2o/latest-stable/h2o-docs/flow.html https://www.analyticsvidhya.com/blog/2021/05/a-step-by-step-guide-to-automl-with-h2o-flow/
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-cheat-sheet
https://codegnan.com/blog/35-best-data-sciecne-tools-for-beginners-to-master/ https://analyticsindiamag.com/free-online-resources-to-learn-automl/
https://analyticsindiamag.com/10-popular-automl-tools-developers-can-use/ https://analyticsindiamag.com/8-best-open-source-tools-for-data-mining/
mlkit-https://firebase.google.com/products/ml runway https://runwayml.com/ fritz https://www.fritz.ai/
obviously https://www.obviously.ai/ createml https://developer.apple.com/machine-learning/create-ml/ makeml https://makeml.app/
superannotate https://superannotate.com/ https://rapidminer.com/ https://monkeylearn.com/monkeylearn-studio/ https://nanonets.com/
GCP Professional ML Engineer certification in 8 days https://ml-rafiqhasan.medium.com/how-i-cracked-the-gcp-professional-ml-engineer-certification-in-8-days-f341cf0bc5a0
Vertex AI, one platform, every ML tool you need https://cloud.google.com/vertex-ai
2.FasterAI,keras,fastai,tesorflow,pytorch
Automated model architecture search tools (e.g. darts, enas) https://awesomeopensource.com/projects/automl
https://github.com/search?q=automl https://www.kdnuggets.com/2016/03/automated-data-science.html https://www.kdnuggets.com/software/automated-data-science.html
Tpot https://github.com/EpistasisLab/tpot
ATOM https://github.com/tvdboom/ATOM https://towardsdatascience.com/how-to-test-multiple-machine-learning-pipelines-with-just-a-few-lines-of-python-1a16cb4686d
mljar-supervised https://github.com/mljar/mljar-supervised
libra end-to-end machine learning process in just one line of code https://github.com/Palashio/libra
featurewiz, boruta_py ,AutoWeka,Auto-Sklearn,AutoGluon,Auto-PyTorch,AutoKeras,auto-tensorflow,Ludwig,MLBox,PyCaret,LightAutoML,FLAML,EvalML,H2O AutoML
GML https://github.com/Muhammad4hmed/GML
auto_ml https://github.com/ClimbsRocks/auto_ml
automl-gs Automating Machine Learning In A Single Line Of Code https://github.com/minimaxir/automl-gs
paddlehub Performing Computer Vision & NLP Tasks in a Single Of Code https://github.com/PaddlePaddle/PaddleHub
pywedge https://github.com/taknev83/pywedge https://towardsdatascience.com/automated-interactive-package-for-eda-modeling-and-hyperparameter-tuning-in-a-few-lines-of-228c561fa63c
LightAutoML https://github.com/sberbank-ai-lab/LightAutoML https://lightautoml.readthedocs.io/en/latest/ https://towardsdatascience.com/lightautoml-preset-usage-tutorial-2cce7da6f936
FLAML fast and lightweight AutoML library https://github.com/microsoft/FLAML
LightAutoML LAMA - automatic model creation framework https://github.com/sberbank-ai-lab/LightAutoML
H2O Hydrogen Torch: A No-code Deep Learning Framework
EvalML is an AutoML library https://github.com/alteryx/evalml https://evalml.alteryx.com/en/stable/ https://www.kdnuggets.com/2021/04/easy-automl-python.html https://www.youtube.com/watch?v=uuYEQqrExBQ https://www.analyticsvidhya.com/blog/2021/05/machine-learning-automation-using-evalml-library/
dataprep Beginners Guide to Automation in Data Science https://www.analyticsvidhya.com/blog/2021/04/beginners-guide-to-automation-in-data-science/
A machine learning tool for automated prediction engineering https://github.com/alteryx/compose
adanet https://github.com/tensorflow/adanet
mljar-supervised https://github.com/mljar/mljar-supervised/ https://www.kdnuggets.com/2021/05/binary-classification-automated-machine-learning.html
ludwig https://github.com/ludwig-ai/ludwig
carefree-learn is a minimal Automatic Machine Learning (AutoML) solution for tabular datasets based on PyTorch https://carefree0910.me/carefree-learn-doc/
autoweka https://github.com/automl/autoweka
ATOM Automated Tool for Optimized Modelling https://github.com/tvdboom/ATOM
autokeras https://autokeras.com/ autoSklearn https://automl.github.io/auto-sklearn/master/
baytune auto-tuning https://github.com/MLBazaar/BTB
storm-tuner Best Hyper Parameters For Deep Learning Model https://github.com/ben-arnao/StoRM
adanet https://github.com/tensorflow/adanet
AlphaPy Automated Machine Learning https://github.com/ScottfreeLLC/AlphaPy
TransmogrifAI https://github.com/salesforce/TransmogrifAI
Hugging Face’s AutoNLP https://www.analyticsvidhya.com/blog/2021/03/a-hands-on-introduction-to-hugging-faces-autonlp-101/
complex Machine Learning model in one line with Libra https://github.com/Palashio/libra
Automated Text Classification with EvalML https://www.kdnuggets.com/2021/04/automated-text-classification-evalml.html
Pywedge A complete package for EDA, Data Preprocessing and Modelling https://towardsdatascience.com/pywedge-a-complete-package-for-eda-data-preprocessing-and-modelling-32171702a1e0
3.awesome-AutoML https://github.com/windmaple/awesome-AutoML , automl-gs github.com/minimaxir/automl-gs
autopandas,Auto-Sklearn,Auto-Pytorch,Auto-ViML,AutoViz,AutoGluon,MLBox,FLAML,EvalML,scikit-optimize,Hyperopt-Sklearn,smac3,alphapy,nni,adanet,ludwig, TPOT,flaml, H2OAutoML ,automl ,LightAutoML,auto keras,MLJAR,PyCaret,Auto-sklearn,SMAC,WALTS
Auto-PyTorch,Keras Tuner,DataRobot, DriverlessAI , MLBox, AutoGluon, autoweka, Amazon Lex,Darwin,AdaNet, Microsoft NNI,GradsFlow,Ludwig,autoai,Get Duet,Qlik AutoML,NeutonAutoML,Clarifai,CreateML,Lobe,ObviouslyAI,RunwayML,neuton automl,TransmogrifAI,Rapid Miner,Dataiku,DataRobot,H2O Driverless,Amazon Lex, BigML,AutoML JADBio,Akkio MLJAR, Tazi.ai,UBER’s Ludwig,ANAI,Google Vizier,Tune,HpBandSter,Hyperopt,Facebook’s HiPlot,Bayesian Optimisation,SmartML,SigOpt,Talos,mlmachine,SHERPA Scikit-Optimize,Microsoft’s NNI,Google’s Vizer,GPyOpt,Hyperopt Metric Optimisation Engine (MOE),Optuna,Ray Tune,Keras Tuner,TransmogrifAI
Automated Tensorflow https://github.com/rafiqhasan/auto-tensorflow
MLBox https://github.com/AxeldeRomblay/MLBox
skycube automl https://skycube.app/
stackml Machine Learning platform in the browser https://stackml.com/
quick_ml https://pypi.org/project/quick-ml/ https://www.quickml.info/
MLJAR https://github.com/mljar/mljar-supervised/ https://towardsdatascience.com/binary-classification-with-automated-machine-learning-1a36e78ba50f
TransmogrifAI https://github.com/salesforce/TransmogrifAI darwin http://drwn.anu.edu.au/
GenoML (AutoML) for Genomics https://genoml.com/ https://github.com/GenoML
baytune https://www.kdnuggets.com/2021/03/automating-machine-learning-model-optimization.html https://github.com/MLBazaar/BTB
adanet https://github.com/tensorflow/adanet
FEDOT Automated modeling and machine learning framework FEDOT https://github.com/nccr-itmo/FEDOT
4.AutoGluon AutoML for Text, Image, and Tabular Data https://analyticsindiamag.com/how-to-automate-machine-learning-tasks-using-autogluon/
AutoGL: The First Ever AutoML Framework for Graph Datasets https://analyticsindiamag.com/meet-autogl-the-first-ever-automl-framework-for-graph-datasets/
Neuton TinyML https://neuton.ai/
- auto sklearn,auto keras,auto Tensorflow,autoLightAutoML,xcessiv,kerastuner ,LAMA, NNI, FEDOT (https://github.com/sberbank-ai-lab/LightAutoML)
deephyper Automating Deep Neural Networks https://github.com/deephyper/deephyper
Keras Tuner or storm-tuner - Decide Number of Hidden Layers And Neuron In Neural Network
AutoNeuro https://autoneuro.challenge-ineuron.in/
ATOM https://towardsdatascience.com/atom-a-python-package-for-fast-exploration-of-machine-learning-pipelines-653956a16e7b https://github.com/tvdboom/ATOM
-
autoviml https://github.com/AutoViML/Auto_ViML https://towardsdatascience.com/autoviml-automating-machine-learning-4792fee6ae1e
deep_autoviml https://github.com/AutoViML/deep_autoviml
𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗺𝗼𝘀𝘁 𝗼𝗳 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 https://github.com/Muhammad4hmed/GML
CodeLess https://pypi.org/project/codeless/ https://github.com/porky5191/codeless_demo_project
AUTORL: AUTOML FOR RL https://www.automl.org/blog-autorl/
-
sweetviz (EDA purpose) - https://pypi.org/project/sweetviz/ https://www.kdnuggets.com/2021/03/know-your-data-much-faster-sweetviz-python-library.html
-
pandasprofiling(display whole EDA) - https://pypi.org/project/pandas-profiling/ https://pandas-profiling.github.io/pandas-profiling/docs/master/rtd/index.html
-
autokeras,AutoSklearn,Neural Network Intelligence
FeatureTools automated feature engineering.
MLBox,Lightwood,mindsdb(machine learning models using SQL queries),mljar-supervised,Ludwig(deep learning models without the need to write code)
AdaNet is a lightweight TensorFlow-based framework
-
pycaret- https://pycaret.org/ https://www.kdnuggets.com/2020/08/build-automl-pycaret.html https://www.kdnuggets.com/2020/08/github-best-automl-ever-need.html https://www.kdnuggets.com/2020/07/5-things-pycaret.html
Machine Learning in Power BI using PyCaret https://www.kdnuggets.com/2020/05/machine-learning-power-bi-pycaret.html
https://www.kdnuggets.com/2020/06/deploy-machine-learning-pipeline-cloud-docker.html https://www.kdnuggets.com/2020/08/github-best-automl-ever-need.html
mindsdb Machine Learning in 5 Lines of Code https://mindsdb.com/
automated feature engineering https://github.com/alteryx/featuretools https://towardsdatascience.com/why-automated-feature-engineering-will-change-the-way-you-do-machine-learning-5c15bf188b96
Featuretools https://www.featuretools.com/
Automate your ML Pipelines with EvalML https://analyticsindiamag.com/automate-your-ml-pipelines-with-evalml/
Aethos — A Data Science Library to Automate your Workflow https://towardsdatascience.com/aethos-a-data-science-library-to-automate-workflow-17cd76b073a4
AutoAI — Automating the AI Workflow to Build & Deploy Machine Learning model https://medium.com/geekculture/autoai-automating-the-ai-workflow-to-build-deploy-machine-learning-model-bb2b727cda28
AutoML toolkit https://github.com/microsoft/nni
LightAutoML LAMA - automatic model creation framework https://github.com/sberbank-ai-lab/LightAutoML https://analyticsindiamag.com/hands-on-python-guide-to-lama-an-automatic-ml-model-creation-framework/
LightAutoML https://github.com/sb-ai-lab/LightAutoML
mljar-supervised Automates Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning https://github.com/mljar/mljar-supervised
MLBox is a powerful Automated Machine Learning python library https://github.com/AxeldeRomblay/MLBox
12.Auto_Timeseries by auto_ts
13.AutoNLP_Sentiment_Analysis by autoviml
14.automl lazypredict https://github.com/shankarpandala/lazypredict
AutoML Toolkit for Graph Datasets & Tasks AutoGL(Auto Graph Learning)https://medium.com/syncedreview/tsinghua-university-releases-first-automl-toolkit-for-graph-datasets-tasks-c61ea0261d78
AutoFeat-https://analyticsindiamag.com/guide-to-automatic-feature-engineering-using-autofeat/
15.https://github.com/mstaniak/autoEDA-resources
mito , dtale
bamboolib or pandas-ui or pandas-summary or pandas_visual_analysis or Dtale(get code also) (python package for easy data exploration & transformation)
Automating EDA using Pandas Profiling, streamlit_pandas_profiling,Sweetviz and Autoviz,DataPrep,vaex,Datapane,Sweetviz,pandas_UI,PandasGUI,Datatable,Dora,Pywedge,D-Tale,lux,Dabl,Pretty pandas,data_describe,Sparkora,AWS Glue DataBrew,speedML,edaviz,Altair,voyager,Mito,Facets,KNIME,lux,datatable,Pandas-visual-analysis,ExploriPy,Holoviews,lux,Dataprep,atoti,QuickDA ,panel-highcharts,Know Your Data,Atoti ,ExploriPy,autoplotter,tensorflow data validation,skimpy,Skim,OpenRefine,Visualizer,autoclean,Autoplotter,dataTile,mito,Bamboolib,TensorFlow Data Validation,speedML,edaviz,pandas-summary,ExploriPy, ipywidgets,ipympl,data_describe,lens,DStack,autoplotter,klib,Datasette,FACETS,TensorFlow Data Validation,Auto Data Exploration and Feature Recommendation Tool,great_expectations,DataProfiler,Datasette,streamlit-aggrid,Quick-EDA,QuickDA,Datatile,Deepnote,PiML,AutoPlotter,Klib,Pivottablejs,Qgrid,facets,Great Expectations,Explainerdashboard,BitRook,AutoPlotter,OmniXAI,tabloo,sidetable,HvPlot,summarytools,fasteda,Rath,Missingno,Sketch,pygwalker,fasteda,Apache Superset,Algorithm-visualizer,perspective,jupyter-datatables,dfgui,AutoProfiler,Datatile,ExploriPy
Three R Libraries for Automated EDA dataMaid,DataExplorer,SmartEDA
fiftyone Highly Interactive Dashboards For Visualizing Datasets and Interpret Model https://towardsdatascience.com/highly-interactive-dashboards-for-visualizing-dataset-and-interpret-model-ce6311ea57ca
interpret Dashboards for Interpreting & Comparing Machine Learning Models https://towardsdatascience.com/dashboards-for-interpreting-comparing-machine-learning-models-ffcfb4c05152
Dataprep https://towardsdatascience.com/dataprep-eda-accelerate-your-eda-eb845a4088bc https://www.analyticsvidhya.com/blog/2021/05/dataprep-library-perform-eda-faster/
explainerdashboard https://towardsdatascience.com/the-quickest-way-to-build-dashboards-for-machine-learning-models-ec769825070d
Facets https://github.com/PAIR-code/facets https://towardsdatascience.com/visualize-your-data-with-facets-d11b085409bc
pywedge https://github.com/taknev83/pywedge https://towardsdatascience.com/automated-interactive-package-for-eda-modeling-and-hyperparameter-tuning-in-a-few-lines-of-228c561fa63c
Datapane makes it simple to build shareable reports from Python https://github.com/datapane/datapane https://towardsdatascience.com/datapanes-new-features-create-a-beautiful-dashboard-in-python-in-a-few-lines-of-code-a3c44523292b https://towardsdatascience.com/introduction-to-datapane-a-python-library-to-build-interactive-reports-4593fd3cb9c8
lux https://medium.com/swlh/automating-exploratory-data-analysis-part-3-d04352b83072 https://pub.towardsai.net/speed-up-eda-with-the-intelligent-lux-37f96542527b
lux Python API for Intelligent Visual Data Discovery https://github.com/lux-org/lux https://analyticsindiamag.com/python-guide-to-lux-an-interactive-visual-discovery/
Automatic EDA https://thecleverprogrammer.com/2021/02/06/automatic-eda-using-python/
Automated Interactive Package for EDA, Modeling, and Hyperparameter Tuning in a few lines of Python Code https://towardsdatascience.com/automated-interactive-package-for-eda-modeling-and-hyperparameter-tuning-in-a-few-lines-of-228c561fa63c
Arena https://github.com/ModelOriented/Arena
https://github.com/mstaniak/autoEDA-resources https://thecleverprogrammer.com/2021/02/06/automatic-eda-using-python/
ExploriPy import EDA-https://analyticsindiamag.com/hands-on-tutorial-on-exploripy-effortless-target-based-eda-tool/
Lens- Statistical Analysis of Data https://analyticsindiamag.com/hands-on-tutorial-on-lens-python-tool-for-swift-statistical-analysis/
Dashboard in Less Than 10 Lines of Code https://towardsdatascience.com/build-dashboards-in-less-than-10-lines-of-code-835e9abeae4b
Plotly Express Interprete data through interactive visualization https://pub.towardsai.net/matplotlib-is-dead-long-life-to-plotly-express-e1671dce0d18
Rich terminal dashboards https://www.willmcgugan.com/blog/tech/post/building-rich-terminal-dashboards/
Explainable AI dashboards https://github.com/oegedijk/explainerdashboard https://www.youtube.com/watch?v=ZgypAMRcmw8
Machine Learning Model Dashboard https://towardsdatascience.com/machine-learning-model-dashboard-4544daa50848
Creating Automated Python Dashboards using Plotly, Datapane, and GitHub Actions https://towardsdatascience.com/creating-automated-python-dashboards-using-plotly-datapane-and-github-actions-ff8aa8b4e3
atoti Python library to quickly build BI analytics dashboards https://docs.atoti.io/latest/tutorial/tutorial.html
interactive dashboards https://medium.com/analytics-vidhya/explainer-dashboard-build-interactive-dashboards-for-machine-learning-models-fda63e0eab9
MitoSheets https://analyticsindiamag.com/guide-to-mitosheets-harnessing-power-of-spreadsheets-in-python/
Datacleaner-https://analyticsindiamag.com/tutorial-on-datacleaner-python-tool-to-speed-up-data-cleaning-process/
Datacleaner :dora ,Voilà -Jupyter Notebooks quickly into standalone web applications , Plotly Dash - for more advanced and production level dashboards
featurewiz(Select the best features from your data set fast with a single line of code) - https://github.com/AutoViML/featurewiz
explainerdashboard https://medium.com/analytics-vidhya/explainer-dashboard-build-interactive-dashboards-for-machine-learning-models-fda63e0eab9
interpret Dashboards for Interpreting & Comparing Machine Learning Models https://hmix13.medium.com/dashboards-for-interpreting-comparing-machine-learning-models-ffcfb4c05152
https://www.kdnuggets.com/2019/07/10-simple-hacks-speed-data-analysis-python.html
Panel - web apps
Automating report generation with Jupyter Notebooks https://medium.com/applied-data-science/full-stack-data-scientist-5-automating-report-generation-with-jupyter-notebooks-919e32e88d18
10 Useful Jupyter Notebook Extensions for a Data Scientist https://towardsdatascience.com/10-useful-jupyter-notebook-extensions-for-a-data-scientist-bd4cb472c25e
Datapane ( Build Interactive Reports) https://towardsdatascience.com/introduction-to-datapane-a-python-library-to-build-interactive-reports-4593fd3cb9c8 https://www.kdnuggets.com/news/index.html
pomegranate probabilistic modelling in Python https://github.com/jmschrei/pomegranate https://www.kdnuggets.com/2020/12/fast-intuitive-statistical-modeling-pomegranate.html
16.CUPY (array process parallel in gpu) https://pypi.org/project/cupy/
17.Dabl-automate the known 80% of Data Science which is data preprocessing, data cleaning, and feature engineering https://pypi.org/project/dabl/
18.dask (parallel comptataion) https://docs.dask.org/en/latest/ https://medium.com/rapids-ai/reading-larger-than-memory-csvs-with-rapids-and-dask-e6e27dfa6c0f#cid=av01_so-nvsh_en-us
pandarallel https://towardsdatascience.com/make-pandas-run-blazingly-fast-3dbcd621f75b
Dask Dataframe and SQL https://docs.dask.org/en/latest/dataframe-sql.html
Swiftapply – Automatically efficient pandas apply operations https://www.kdnuggets.com/2018/04/swiftapply-automatically-efficient-pandas-apply-operations.html
Dask CUDA
Numba https://github.com/numba/numba https://www.youtube.com/watch?v=3O-Pvnrbsu0 https://www.analyticsvidhya.com/blog/2021/04/numba-for-data-science-make-your-py-code-run-1000x-faster/
Cython,Numba,PyPy,ray,loky,Dask,p_tqdm (aka Pathos + tqdm),modin,connectorx,cudf, cuML
Reducing Pandas memory https://pythonspeed.com/articles/pandas-load-less-data/ https://www.youtube.com/watch?v=HNE0qHJ9A9o
Speed up Scikit-Learn Model Training https://www.kdnuggets.com/2021/02/speed-up-scikit-learn-model-training.html
mpire Python package for easy multiprocessing, but faster than multiprocessing https://github.com/Slimmer-AI/mpire
thundergbm Fast GBDTs and Random Forests on GPUs https://github.com/Xtra-Computing/thundergbm
thundersvm https://github.com/Xtra-Computing/thundersvm
NumPy API on TensorFlow https://www.tensorflow.org/guide/tf_numpy https://www.youtube.com/watch?v=mgY46AEXnG0
change to proper dtypes,usecols of required only reduce size
Better Data Storage : CSV,Parquet,fastparquet,Feather,lance,HDF5,Apache Arrow,Lance
pandas chunksize,Pandas vectorization,Numpy Vectorization, multiprocessing,airflow,celery,Modin ,Vaex,ray,Dask,PyPolars,Polars,spark,pyspark,Koalas,Cython , cuML,cuDF,cupy,mars,ray,Caching,rapids,joblib,snorkel,arrow,Pyarrow,Ponder,Apache Arrow,Datatable,Fastparquet,dampr,Data Table , pandarallel ,Parallel-Pandas,numba,bolt, numexpr,ipython parallel,Nim,speedML,ConnectorX , apache arrow,jax,Pandas-on-Spark,Terality,swifter,partial_fit(),Numba,numexpr,mtalgDask,PyArrow, and PySpark,Fugue,NumPy vectorization,Pandas vectorization,datatable,RAPIDS,Swifter,taichi,scikit-learn-intelex,𝚏𝚞𝚐𝚞𝚎,bottleneck,Pandarallel,Datatable,Pyspark,Koalas,Cylon,Ibis,pandarallel,Blaze,Odo,multiprocessing,joblib,bottleneck,Mapply,Bottleneck,DuckDB,DataFusion, Blaze,Dremio,DuckDB,dbt,Ponder,Daft https://www.youtube.com/watch?v=eJyjB3cNIB0&feature=youtu.be
deal with Big Data Optimize dataframes,Use only required columns,Chunking data,Sparse data formats,Better Data file formats(Parquet,Feather,HDF5),Pandas alternates(Modin,vaex,dask,spark),Intel(R) extension for sklearn, Apply Vectorized,Numba,Rapids cuDF
composer library of algorithms to speed up neural network training https://github.com/mosaicml/composer
ColossalAI A Unified Deep Learning System for Large-Scale Parallel Training https://github.com/hpcaitech/ColossalAI
19.dataprep (Understand your data with a few lines of code in seconds)
data-preparation-tools - https://improvado.io/blog/data-preparation-tools
20.Dora library is another data analysis library designed to simplify exploratory data analysis. https://pypi.org/project/Dora/
23.FlashText (A library faster than Regular Expressions for NLP tasks) https://pypi.org/project/flashtext/
24.Guietta (tool that makes simple GUIs simple) https://pypi.org/project/guietta/
pandas-visual-analysis -https://analyticsindiamag.com/hands-on-guide-to-pandas-visual-analysis-way-to-speed-up-data-visualization/
25.hummingbird (make code fastly exexcute) https://pypi.org/project/Hummingbird/ https://analyticsindiamag.com/guide-to-hummingbird-a-microsofts-library-for-expediting-traditional-machine-learning-models/
CUML- increase the speed of training your machine learning model https://towardsdatascience.com/train-your-machine-learning-model-150x-faster-with-cuml-69d0768a047a
https://docs.rapids.ai/api/cuml/stable/
modin https://www.kdnuggets.com/2021/03/speed-up-pandas-modin.html
Datatable speed up pandas https://www.youtube.com/watch?v=mQi6QIGGJ5U
Process large datasets without running out of memory https://pythonspeed.com/memory/?utm_medium=email&utm_source=topic+optin&utm_campaign=awareness&utm_content=20210426+data+ai+nl&mkt_tok=MTA3LUZNUy0wNzAAAAF8rA-uJucI5nYkInNB60OO8SozgyRZZ2ptfW-Dt-5HR3I0ysFHju2OYpeK_JZRtxcnmHGSefwL-1zg9Be3zse6zZVklh3zcWYSCxLRvJqd5LfAJMaF
Snap ML — Speed Up Model Training https://medium.com/ibm-data-ai/snap-ml-speed-up-model-training-2ef36fbbf101
26.memory-profiler (tell memory consumption line by line) https://pypi.org/project/memory-profiler/
Cython A Speed-Up Tool for your Python Function https://towardsdatascience.com/cython-a-speed-up-tool-for-your-python-function-9bab64364bfd
PyPy Run Your Python Code as Fast as C https://towardsdatascience.com/run-your-python-code-as-fast-as-c-4ae49935a826
Python Tricks for Keeping Track of Your Data https://towardsdatascience.com/python-tricks-for-keeping-track-of-your-data-aef3dc817a4e
27.numexpr (incerease speed of execution of numpy) https://github.com/pydata/numexpr
pypolars instead of pandas (beating-pandas-performance) https://www.youtube.com/watch?v=1-O_KnLZEso https://towardsdatascience.com/3x-times-faster-pandas-with-pypolars-7550e605805e
50X speed up your Pandas apply function https://github.com/jmcarpenter2/swifter
sklearn 100x Faster https://www.kdnuggets.com/2019/09/train-sklearn-100x-faster.html
JAX Autograd and XLA, facilitating high-performance machine learning research https://github.com/google/jax
Numba (optimise performance of numpy and high performance python compiler) http://numba.pydata.org/
Pyston project open sources its faster Python https://www.infoworld.com/article/3618169/pyston-project-open-sources-its-faster-python.html
28.pandarallel (simple and efficient tool to parallelize your pandas computation on all your CPUs) https://pypi.org/project/pandarallel/
Pandarallel, Pandarallel’s parallel_apply()
29.PDFTableExtract(by PyPDF2) https://github.com/ashima/pdf-table-extract
Camelot-https://towardsdatascience.com/extracting-tabular-data-from-pdfs-made-easy-with-camelot-80c13967cc88
30.PyImpuyte(Python package that simplifies the task of imputing missing values in big datasets) https://pypi.org/project/PyImpuyte/
31.libra(Automates the end-to-end machine learning process in just one line of code) https://pypi.org/project/libra/
32.debug code by puyton -m pdp -c continue
33.cURL (This is a useful tool for obtaining data from any server via a variety of protocols including HTTP.) https://stackabuse.com/using-curl-in-python-with-pycurl/
34.csvkit https://pypi.org/project/csvkit/
35.IPython IPython gives access to enhanced interactive python from the shell.
36.pip install faker (Create our own Dataset) https://pypi.org/project/Faker/
37.Python debugger %pdb
38.𝚟𝚘𝚒𝚕𝚊-From notebooks to standalone web applications and dashboards https://voila.readthedocs.io/en/stable/ https://github.com/voila-dashboards/voila
39.𝚝𝚜𝚕𝚎𝚊𝚛𝚗 for timeseries data https://github.com/tslearn-team/tslearn
40.texthero text-based dataset in Pandas Dataframe quickly and effortlessly https://github.com/jbesomi/texthero
41.𝚔𝚊𝚕𝚎𝚒𝚍𝚘(web-based visualization libraries like your Jupyter Notebook with zero dependencies) https://pypi.org/project/kaleido/
42.Vaex- Reading And Processing Huge Datasets in seconds https://github.com/vaexio/vaex
43.Uber’s Ludwig is an Open Source Framework for Low-Code Machine Learning https://eng.uber.com/introducing-ludwig/
44.Google's TAPAS, a BERT-Based Model for Querying Tables Using Natural Language https://github.com/google-research/tapas
45.RAPIDS open GPU Data Science https://rapids.ai/
RAPIDS cuML,cudf
tick is a lightweight machine learning library https://x-datainitiative.github.io/tick/
modular machine learning framework http://www.pybrain.org/docs/
machine learning framework It supports several programming languages notably: Python, R, Java, Scala, Ruby and Lua Shogun https://github.com/shogun-toolbox/shogun/
46.pyforest Lazy-import of all popular Python Data Science libraries. Stop writing the same imports over and over again. https://pypi.org/project/pyforest/0.1.1/
47.Modin Get faster Pandas with Modin https://github.com/modin-project/modin
48.Text2Code for Jupyter notebook - https://github.com/deepklarity/jupyter-text2code , https://towardsdatascience.com/data-analysis-made-easy-text2code-for-jupyter-notebook-5380e89bb493
49.Openrefine Tool-For Data Preprocessing Without Code https://analyticsindiamag.com/openrefine-tutorial-a-tool-for-data-preprocessing-without-code/
50.Microsoft Releases Latest Version Of DeepSpeed deep learning optimisation library known as DeepSpeed- https://github.com/microsoft/DeepSpeed
51.4-pandas-tricks-https://towardsdatascience.com/4-pandas-tricks-that-most-people-dont-know-86a70a007993
53.autoplotter is a python package for GUI based exploratory data analysis-https://github.com/ersaurabhverma/autoplotter
54.3 NLP Interpretability Tools For Debugging Language Models-https://www.topbots.com/nlp-interpretability-tools/
55.New Algorithm For Training Sparse Neural Networks (RigL)-https://analyticsindiamag.com/rigl-google-algorithm-neural-networks/
56.Read Data from pdf and Word-PyPDF2,PDFMiner,PDFQuery,tabula-py,pdflib for Python,PDFTables,PyFPDF2
OpenCV to Extract Information From Table Images-https://analyticsindiamag.com/how-to-use-opencv-to-extract-information-from-table-images/
57.Text Annotation-https://towardsdatascience.com/tortus-e4002d95134b
58.GDMix, A Framework That Trains Efficient Personalisation Models - https://analyticsindiamag.com/linkedin-open-sources-gdmix-a-framework-that-trains-efficient-personalisation-models/
59.Learn Machine Learning Concepts Interactively-https://towardsdatascience.com/learn-machine-learning-concepts-interactively-6c3f64518da2
60.Folium, Python Library For Geographical Data Visualization-https://analyticsindiamag.com/hands-on-tutorial-on-folium-python-library-for-geographical-data-visualization/
61.GPU Technology Conference (GTC) Keynote Oct 2020-https://www.youtube.com/watch?v=Dw4oet5f0dI&list=PLZHnYvH1qtOYOfzAj7JZFwqtabM5XPku1
62.jiant nlp task-https://github.com/nyu-mll/jiant
63.painted your machine learning model-https://koaning.github.io/human-learn/
64.Vector AI-https://github.com/vector-ai/vectorai
65.NVIDIA NeMo(for Conversational AI)-https://github.com/NVIDIA/NeMo
66.Deep Learning Models Without Coding(DeepCognition)-https://analyticsindiamag.com/how-to-use-deepcognition-to-build-drag-and-drop-deep-learning-models-without-coding/
67.100 Machine Learning Projects-https://medium.com/@amankharwal/100-machine-learning-projects-aff22b22dd6e
68.Question generation using Natural Language Processing-https://github.com/ramsrigouthamg/Questgen.ai
69.PixelLib(image segmentation,Blur Background,Gray Background,Background Colour Change,Background Change)-https://github.com/ayoolaolafenwa/PixelLib
70.High-Resolution 3D Human Digitization-https://shunsukesaito.github.io/PIFuHD/
71.AI model that translates 100 languages without relying on English data - https://ai.facebook.com/blog/introducing-many-to-many-multilingual-machine-translation/
72.800 free textbooks - https://open.umn.edu/opentextbooks
73.TensorDash is an application that lets you remotely monitor your deep learning model's metrics and notifies you when your model training is completed or crashed.
https://github.com/CleanPegasus/TensorDash
74.YellowBrick -select features, tune hyperparameters, select the best models, and understand the performance metrics.
75.Freely Available Python Books-https://rajukumarmishrablog.com/freely-available-python-books/
Collection of Python Cheat Sheets- https://rajukumarmishrablog.com/collection-of-python-cheat-sheets/
76.Add External Data to Your Pandas Dataframe - https://towardsdatascience.com/add-external-data-to-your-pandas-dataframe-with-a-one-liner-f060f80daaa4
https://www.openblender.io/#/welcome
77.visualize the model architecture-https://github.com/PerceptiLabs/PerceptiLabs
78.Train Conversational AI in 3 lines of code with NeMo and Lightning-https://towardsdatascience.com/train-conversational-ai-in-3-lines-of-code-with-nemo-and-lightning-a6088988ae37
79.Machine Learning for Healthcare by mit-https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-s897-machine-learning-for-healthcare-spring-2019/
80.pydot is an interface to Graphviz ,AutoGraph-Easy control flow for graphs,Neo4j-Graph Data Science Library,pyRDF2Vec-Representations of Entities in a Knowledge Graph,igraph,NetworkX,euler,pyvis,NEuler: No-code graph algorithms,dgl ease deep learning on graph,Graph4nlp,Graph-tool,Networkit,Igraph
PyG (PyTorch Geometric) Graph Neural Network Library for PyTorch https://github.com/pyg-team/pytorch_geometric
7 Open Source Libraries for Deep Learning Graphs https://www.kdnuggets.com/2021/07/7-open-source-libraries-deep-learning-graphs.html
GeometricFlux.jl,PyTorch GNN, Jraph,Spektral,Graph Nets,Deep Graph Library , PyTorch Geometric
https://www.tensorflow.org/neural_structured_learning https://github.com/deepmind/graph_nets https://deepmind.com/research/open-source/graph-nets-library
https://www.kdnuggets.com/2019/09/5-graph-algorithms-data-scientists-know.html https://towardsdatascience.com/visualizing-networks-in-python-d70f4cbeb259
Pyviz https://towardsdatascience.com/interactive-network-visualization-757af376621
AutoGL: The First Ever AutoML Framework for Graph Datasets https://analyticsindiamag.com/meet-autogl-the-first-ever-automl-framework-for-graph-datasets/
https://analyticsindiamag.com/complete-guide-to-autogl-the-latest-automl-framework-for-graph-datasets/ http://mn.cs.tsinghua.edu.cn/AutoGL/
Graph Neural Networks, PySpark, Neural Cellular Automata, FB Prophet, Google Cloud and NLP codes https://github.com/RubensZimbres/Repo-2021
AmpliGraph: A Machine Learning Library For Knowledge Graphs https://analyticsindiamag.com/guide-to-ampligraph-a-machine-learning-library-for-knowledge-graphs/
open-source project for analysis of graphs or networks GrasPy / graspologic https://graspy.neurodata.io/
Pykg2vec: A Python Library for Knowledge Graph Embedding https://analyticsindiamag.com/pykg2vec/
https://www.kdnuggets.com/2019/05/60-useful-graph-visualization-libraries.html https://www.kdnuggets.com/2015/06/top-30-social-network-analysis-visualization-tools.html
84.Google Introduces Document AI (DocAI) https://www.marktechpost.com/2020/11/05/google-introduces-document-ai-docai-platform-for-automated-document-processing/
85.100 Machine Learning Projects-https://amankharwal.medium.com/100-machine-learning-projects-aff22b22dd6e
86.https://towardsdatascience.com/25-hot-new-data-tools-and-what-they-dont-do-31bf23bd8e56
87.Opacus: A high-speed library for training PyTorch models-https://ai.facebook.com/blog/introducing-opacus-a-high-speed-library-for-training-pytorch-models-with-differential-privacy
88.lazynlp https://github.com/chiphuyen/lazynlp
90.Pseudo-Labeling (deal with small datasets)https://towardsdatascience.com/pseudo-labeling-to-deal-with-small-datasets-what-why-how-fd6f903213af
91.Project List A - Comparatively Easy Wine Quality Analysis,Boston Housing Prediction,Spam Email Classification,Survival Prediction - Titanic Disaster,Stock Market Prediction Class of Flower Prediction,Bigmart Sales Prediction,Air Pollution Prediction,IMDB Prediction,Optimizing Product Price,Web Traffic Time Series Forecasting,Insurance Purchase Prediction,Tweet Classification
Project List B - Comparatively Difficult,Domain-Specific Chatbot,Fake News Detection,Human Action Recognition,Video Classification,Driver Drowsiness Detection,Medical Report Gen Using CT Scans,Sign Language Detection,Image Caption Generator,Celebrity Voice Prediction,Speech Emotion Recognition,Job Recommendation System,Interest Level in Rental Properties,Google Ads Keywords Generator
https://ml-showcase.paperspace.com/ https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code
https://dev.to/hb/30-machine-learning-ai-data-science-project-ideas-gf5 https://www.theinsaneapp.com/2021/01/top-30-ai-and-ml-projects-for-2021.html
https://medium.com/the-innovation/130-machine-learning-projects-solved-and-explained-605d188fb392 https://medium.com/coders-camp/96-python-projects-with-source-code-4069eb58beef
https://thecleverprogrammer.com/machine-learning/ https://www.kdnuggets.com/2020/03/20-machine-learning-datasets-project-ideas.html
https://data-flair.training/blogs/machine-learning-datasets/# https://data-flair.training/blogs/machine-learning-project-ideas/
https://data-flair.training/blogs/artificial-intelligence-ai-tutorial/ https://www.theinsaneapp.com/2020/11/data-science-projects-with-source-code.html
https://data-flair.training/blogs/cartoonify-image-opencv-python/ https://data-flair.training/blogs/python-project-calorie-calculator-django/
https://www.theinsaneapp.com/2020/11/machine-learning-projects-with-source-codes.html https://www.theinsaneapp.com/2020/11/data-science-projects-with-source-code.html
https://medium.com/coders-camp/20-deep-learning-projects-with-python-3c56f7e6a721 https://amankharwal.medium.com/12-machine-learning-projects-on-object-detection-46b32adc3c37
https://amankharwal.medium.com/7-python-gui-projects-for-beginners-87ae2c695d78 https://github.com/Kushal997-das/Project-Guidance
https://amankharwal.medium.com/20-machine-learning-projects-for-portfolio-81e3dbd167b1 https://amankharwal.medium.com/4-chatbot-projects-with-python-5b32fd84af37
https://amankharwal.medium.com/30-python-projects-solved-and-explained-563fd7473003
https://www.aiquotient.app/projects https://www.aiquotient.app/ https://www.mltut.com/best-machine-learning-projects-for-beginners/
https://medium.com/coders-camp/20-machine-learning-projects-on-nlp-582effe73b9c
93.The Linux Command Handbook-https://www.freecodecamp.org/news/the-linux-commands-handbook/
94.130 Machine Learning Projects Solved and Explained-https://medium.com/the-innovation/130-machine-learning-projects-solved-and-explained-605d188fb392
95.DataBrew-do drag-and-drop data cleansing
96.stratascratch- https://www.stratascratch.com/
97.5 ways to celebrate TensorFlow's 5th birthday-https://blog.google/technology/ai/5-ways-celebrate-tensorflows-5th-birthday/
98.TensorFlow.js: Machine Learning in Javascript https://blog.tensorflow.org/2018/03/introducing-tensorflowjs-machine-learning-javascript.html
99.Language Interpretability Tool open-source platform for visualization and understanding of NLP models - https://pair-code.github.io/lit/
100.Deep Learning Hardware Guide https://towardsdatascience.com/another-deep-learning-hardware-guide-73a4c35d3e86
101.johnsnowlabs- https://nlp.johnsnowlabs.com/ https://nlp.johnsnowlabs.com/docs/en/quickstart https://nlp.johnsnowlabs.com/docs/en/licensed_release_notes
104.Clarifai-https://www.clarifai.com/ https://analyticsindiamag.com/clarifai/
105.rapidly build and deploy machine learning models https://analyticsindiamag.com/top-10-datarobot-alternatives-one-must-know/
106.Hive Data full-stack AI https://thehive.ai/hive-data
107.real-time remote service to get the Keras callbacks to the telegram including the details of metrics https://github.com/ksdkamesh99/TensorGram
108.Language Interpretability Tool - https://pair-code.github.io/lit/demos/
109.Docly will handle the comments http://thedocly.io/
110.machine-learning-roadmap-2020 https://whimsical.com/machine-learning-roadmap-2020-CA7f3ykvXpnJ9Az32vYXva
112.freecodecamp - https://www.freecodecamp.org/learn
113.image_to_string (pytesseract)
Extract Tables in PDFs to pandas DataFrames - tabula-py
114.NLP Pipelines in a single line of code https://medium.com/analytics-vidhya/nlp-pipelines-in-a-single-line-of-code-500b3266ac7b
116.aitextgen #for ai text generation
117.http://introtodeeplearning.com/ http://cs231n.stanford.edu/ http://web.stanford.edu/class/cs224n/index.html#schedule https://www.youtube.com/playlist?list=PLkFD6_40KJIwhWJpGazJ9VSj9CFMkb79A https://www.youtube.com/playlist?list=PLkFD6_40KJIwhWJpGazJ9VSj9CFMkb79A https://www.youtube.com/playlist?list=PLwRJQ4m4UJjPiJP3691u-qWwPGVKzSlNP https://www.youtube.com/playlist?list=PLoROMvodv4rMC6zfYmnD7UG3LVvwaITY5
117.https://data-flair.training/blogs/data-science-tutorials-home
119.Pystiche - Create Your Artistic Image Using Pystiche https://analyticsindiamag.com/pystiche/ https://pystiche.readthedocs.io/en/latest/index.html
120.Low Light Image Enhancement using Python & Deep Learning https://github.com/soumik12345/MIRNet/ https://www.youtube.com/watch?v=b5Uz_c0JLMs
121.Koalas on Apache Spark - Pandas API https://www.youtube.com/watch?v=kOtAMiMe1JY&t=482s https://koalas.readthedocs.io/en/latest/
122.DALL·E https://openai.com/blog/dall-e/ https://analyticsindiamag.com/comprehensive-guide-to-dall-e-by-openai-creating-images-from-text/
https://github.com/lucidrains/big-sleep https://github.com/lucidrains/deep-daze https://www.youtube.com/watch?v=lVR5kN7SjQ8&feature=youtu.be
DALL·E Mini,GPT-3,Dalle-2,Dalle-3,Imagen,RE-IMAGEN,Parti,Midjourney,Craiyon,Make-A-Scene,Imagen,DALL-E,Imagen, NUWA-Infinity,Make a Scene,Cogview 2,VQGAN,VQGAN-Clip,Latent-Diffusion,Parti,MidJourney,Ultraleap’s Midjourney, Hugging Face’s Craiyon, Meta’s Make-A-Scene and Google’s Imagen,CogVideo,Big Sleep,Disco,Stable Diffusion,fast-stable-diffusion,DreamStudio,CodeFormer,DreamBooth,Tiktok’s Greenscreen,textual_inversion,GauGAN2,Stable-Craiyon,Disco Diffusion,DreamBooth,AI Greenscreen,Wonder,Nightcafe,Midjourney, craiyon,loab,Starry AI,Dream By,Wombo,Nightcafe,Pixray,Deep Dream,Stable Diffusion,DreamFusion,Make-A-Video,Imagen Video,Midjourney,CogVideo,ERNIE-ViLG 2.0,eDiffi,pixray,starryai,promptoMANIA,starry.ai,NightCafe,Artbreeder,wombo.ai,Muse,BlueWillow,StyleGAN-T,GigaGAN,DeepFloyd IF, Bing Image Creator,Craiyon,InstantArt,Pixray,Blue Willow,Playground AI,Picsart,Perfusion AI,XGen-Image,Ideogram AI,DeciDiffusion,lexica
https://pharmapsychotic.com/tools.html https://airtable.com/shrDxAxCCxAZVtMnt/tbl3FzgFjvvuYZMm9 https://www.marktechpost.com/2022/10/05/top-artificial-intelligence-ai-based-text-to-image-generators/
text to video,images,audio,3D: Adobe firefly,NVIDIA Picasso,Runway
text to video : CogVideo,Make-A-Video,Phenaki,Imagen Video,DreamFusion,Phenak,CogVideo,GODIVA,NÜWA,Google UniTune (fine-tuned Imagen),Synthesia,Lumen5,Flixclip,Elai,Veed.io,Kaiber,Genmo,LeiaPix,Glia Cloud,Stable Diffusion Videos,Synthesia,InVideo,Lumen5,Designs.ai,Pictory,Wisecut,Veed.io,Fliki,Shap-e,dalle,pointe,AdaMPI,AudioGen
3D Models from Text : DreamFusion,CLIP-Mesh,Point-E,Magic3D,Text2Mesh,CLIP-Mesh,Neuralangelo
Text-to-Audio : Audiogen,diffsound,GliaCloud,Synthesia,InVideo,Synths Video,VEED.IO,Lumen5,Pictory,Designs.ai,Wisecut,Replica,Speechify,Murf,Play.ht,Lovo.ai,VALL-E,VALL-E X,MusicLM, SingSong, Moûsai 2, AudioLDM, and EPIC-SOUND,Audio-LDM
Top 12 AI Music Generators :MusicLM – Google’s Text to Music Generator,Soundraw.io,Amper Music,AIVA,Humtap,Amadeus Code,Computoser,Google’s Magenta ,Chrome’s Song Maker,Generative.FM,MuseNet
Text-to-Motion : MotionCLIP,Language2Pose
Text-to-PowerPoint : ChatBCG
Mubert Text to Music https://github.com/MubertAI/Mubert-Text-to-Music ,MusicLM,MusicGen
Music generator AIVA,Amper AI,Jukebox,Soundraw,Evoke, AudioML,EnCodec
Text generators Frase Io,Peppertype,Rytr,Jasper,Copy.ai,ChatGPT
Beginner’s Guide to the CLIP Model https://www.kdnuggets.com/2021/03/beginners-guide-clip-model.html https://www.kdnuggets.com/2021/03/multilingual-clip--huggingface-pytorch-lightning.html
StyleCLIP: Text Driven Image Manipulation https://analyticsindiamag.com/guide-to-styleclip-text-driven-image-manipulation/
123.SpeechBrain https://speechbrain.github.io/
124.Real-Time High-Resolution Background Replacement https://analyticsindiamag.com/introducing-real-time-high-resolution-background-replacement/ https://github.com/PeterL1n/BackgroundMattingV2
125.greppo Build & deploy geospatial applications quick and easy. https://github.com/greppo-io/greppo
126.Online tools to create mind-blowing AI art https://analyticsindiamag.com/online-tools-to-create-mind-blowing-ai-art/
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