scikit-learn-python

There are 10 repositories under scikit-learn-python topic.

ML-For-Beginners
microsoft / ML-For-Beginners
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
data-science education machine-learning machine-learning-algorithms machinelearning machinelearning-python ml python r scikit-learn scikit-learn-python
Language:HTML 66873
dataprofessor / code
Compilation of R and Python programming codes on the Data Professor YouTube channel.
shiny exploratory-data-analysis python streamlit r datascience data-science machinelearning machine-learning python-data-science data-science-python dataprofessor data-professor scikit-learn scikit-learn-python pandas
Language:Jupyter Notebook 870
mikekeith52 / scalecast
The practitioner's forecasting library
python time-series machine-learning data-science deep-learning forecasting keras scikit-learn mase msis smape auto-ml easy-to-use scikit-learn-python vecm lstm pandas recurrent-neural-networks
Language:Python 311
SuperKogito / Voice-based-gender-recognition
:sound: :boy: :girl:Voice based gender recognition using Mel-frequency cepstrum coefficients (MFCC) and Gaussian mixture models (GMM)
gender-recognition gender-detection gender-classification gmm mfcc gender-recognition-by-voice voice mel-frequencies gaussian-mixture-models signal machine-learning data-science vocal speech gender scikit-learn scikit-learn-python speaker
Language:Python 184
flo7up / relataly-public-python-tutorials
Beginner-friendly collection of Python notebooks for various use cases of machine learning, deep learning, and analytics. For each notebook there is a separate tutorial on the relataly.com blog.
machine-learning deep-learning time-series-forecasting time-series-prediction recurrent-neural-networks stock-market-prediction twitter-bot churn-prediction keras-tensorflow scikit-learn-python python stock-price-prediction feature-selection data-science neural-network neural-networks time-series openai reinforcement-learning openai-api
Language:Jupyter Notebook 140
dimitrismistriotis / alt-profanity-check
A fast, robust library to check for offensive language in strings, dropdown replacement of "profanity-check".
profanity-detection python python3 scikit-learn-python
Language:Python 59
Machine-Learning-with-Scikit-Learn-Python-3.x
reddyprasade / Machine-Learning-with-Scikit-Learn-Python-3.x
In general, a learning problem considers a set of n samples of data and then tries to predict properties of unknown data. If each sample is more than a single number and, for instance, a multi-dimensional entry (aka multivariate data), it is said to have several attributes or features. Learning problems fall into a few categories: supervised learning, in which the data comes with additional attributes that we want to predict (Click here to go to the scikit-learn supervised learning page).This problem can be either: classification: samples belong to two or more classes and we want to learn from already labeled data how to predict the class of unlabeled data. An example of a classification problem would be handwritten digit recognition, in which the aim is to assign each input vector to one of a finite number of discrete categories. Another way to think of classification is as a discrete (as opposed to continuous) form of supervised learning where one has a limited number of categories and for each of the n samples provided, one is to try to label them with the correct category or class. regression: if the desired output consists of one or more continuous variables, then the task is called regression. An example of a regression problem would be the prediction of the length of a salmon as a function of its age and weight. unsupervised learning, in which the training data consists of a set of input vectors x without any corresponding target values. The goal in such problems may be to discover groups of similar examples within the data, where it is called clustering, or to determine the distribution of data within the input space, known as density estimation, or to project the data from a high-dimensional space down to two or three dimensions for the purpose of visualization (Click here to go to the Scikit-Learn unsupervised learning page).
sklearn machine-learning machine-learning-algorithms regression python-3 python unsupervised-learning supervised-learning scikit-learn classification prediction vector semi-supervised-learning reinforcement-learning scikit-image scikit-learn-python scikit-learn-api scikit-model consists
Language:Jupyter Notebook 52
SuperKogito / Voice-based-speaker-identification
:sound: :boy: :girl: :woman: :man: Speaker identification using voice MFCCs and GMM
speaker-identification speaker-recognition gmm mfcc voice mel-frequency-cepstral-coefficients mel-frequencies gaussian-mixture-models signal machine-learning vocal speech scikit-learn scikit-learn-python
Language:Python 48
BaseMax / ImageRecognitionAI
Recognition of the images with artificial intelligence includes train and tests based on Python.
recognition recognition-color recognition-py python matplotlib numpy opencv opencv-python pyparsing scikit-learn scikit-learn-python scipy sklearn artificial-intelligence artificial-intelligence-algorithms image-processing image-processing-library image-procesing image-processing-python
Language:Python 13
niitsuma / delayedsparse
Efficient sparse matrix implementation for various "Principal Component Analysis"
correspondence-analysis principal-component-analysis sparse-matrix canonical-correlation-analysis scikit-learn-python python ca pca cca svd
Language:Python 11
Machine_Learning
Lawrence-Krukrubo / Machine_Learning
Machine learning is the sub-field of Computer Science, that gives Computers the ability to learn without being explicitly programmed (Arthur samuel, American pioneer in the field of Computer gaming and AI , coined the term Machine Learning in 1959, while at IBM )
supervised-learning unsupervised-machine-learning linear-regression classification-algorithm clustering-algorithm knearest-neighbor-algorithm logistic-regression scikit-learn-python python tensorflow
Language:Jupyter Notebook 7
UtsavMurarka / MXene-machine-learning
Classification of MXenes into metals and non-metals based on physical properties
physics molecules atoms machine-learning deep-learning neural-network random-forest decision-trees logistic-regression knn-classifier machinelearning-python scikit-learn scikitlearn-machine-learning sklearn sklearn-library sklearn-classify scikit-learn-python machine-learning-algorithms machinelearning atomic-physics
Language:Jupyter Notebook 5
rifatSDAS / satellite_machine_learning
Unsupervised and supervised learning for satellite image classification
satellite-data rasterio classifications kmeans-clustering support-vector-machines support-vector-classifier scikitlearn-machine-learning scikit-learn scikit-learn-python sentinels sentinel-2 sentinel-1 sentinel-3
Language:Jupyter Notebook 4
chandakakshat / Digital-Kisaan-Portal
A web application designed to support farmer-community with Intelligent Machine Learning technologies, providing live crop recommendation and prediction system, facilitating farmers with online community support and chat bot based on Artificial Intelligence. It also Integrates an on-demand news feed page aiding for socializing within the farmer community.
machine-learning scikit-learn python aws-lambda aws-cli aws-s3 aws aws-cloudformation django bootstrap html-css-javascript scikit-learn-python
Language:Jupyter Notebook 3
dpscience / DMLLTDetectorPulseDiscriminator
DMLLTDetectorPulseDiscriminator - A supervised machine learning approach for shape-sensitive detector pulse discrimination in lifetime spectroscopy applications
lifetime-spectroscopy machine-learning artificial-intelligence pulse-detection discriminator detector materials-science lifescience positron-lifetime-spectra fluorescence-microscopy-imaging fluorescence-correlation high-energy-physics time-of-flight supervised-machine-learning gaussian-naive-bayes-implementation positron-emission-tomography scikitlearn-machine-learning scikit-learn scikit-learn-python pulse-shape-discrimination
Language:Python 3
IEEE-ECG-Ensemble-XGBoost
souvikmajumder26 / IEEE-ECG-Ensemble-XGBoost
👨‍💻 Developed AI Models - Ensemble of Random Forest & SVM and XGBoost classifiers to classify five types of Arrhythmic Heartbeats from ECG signals - published by IEEE.
machine-learning data-science python python3 scikit-learn scikitlearn-machine-learning scikit-learn-python numpy pandas matplotlib ensemble-learning ensemble-model ensemble-machine-learning ensemble-classifier random-forest svm svm-classifier support-vector-machine svm-model xgboost
Language:Jupyter Notebook 3
tanvirnwu / ML-Algorithms--Scikit-learn--Python
This folder contains the basic algorithms of ML implemented with Python.
ai learning machine machine-learning ml scikit-learn scikit-learn-python scikitlearn-machine-learning scikitlearn
Language:Jupyter Notebook 3
AbhishekRS4 / ML_water_potability
ML model deployment using docker, kubernetes; API deployment with FastAPI; and MLOps using MLFlow for water potability dataset
machine-learning machine-learning-model machine-learning-model-deployment machine-learning-model-management scikit-learn-python water-potability water-quality mlflow mlflow-model mlflow-sklearn docker-deployment kubernetes-deployment fastapi-docker
Language:Python 2
AjayZinngg / simple-nltk-chatbot
A Q&A based chatbot which queries the database to find responses for similar questions asked by the users
python3 nltk sqlite3 flask scikit-learn-python
Language:Python 2
AMPA-ML-Team / PD-Classification
Codes for "Parkinson’s Disease Diagnosis: Effect of Autoencoders to Extract Features from Vocal Characteristics"
python3 keras-tensorflow scikit-learn-python mlp svm xgboost autoencoder parkinsons-disease uci-machine-learning ensemble-learning
Language:Python 2
AshishPandey88 / Nanda-Devi-Glacier-Loss
Feburary 7,2021 Ecological Disaster (Nanda Devi Glacier, IND: 7,108 m above sea level). Satellite image analysis using the methodology of image segmentation shows that the Glacier cover in Nanda Devi has substantially decreased over the last 4 decades. It has gone down from 43% in Year 1984 to 20% in Year 2022 (in relation to the captured area in image)
climate-change climate-model climate-science glacier-modelling glaciers image image-processing image-segmentation satellite-imagery scikit-image scikit-learn-python spatial-analysis
Language:Jupyter Notebook 2
machine_learning_with_sklearn
dblabs-mcgill-mila / machine_learning_with_sklearn
Explore and understand the Machine Learning concepts through the prism of sklearn, one notebook at a time.
educational-project jupyter-notebook machine-learning mcgill-university mila python3 scikit-learn scikitlearn-machine-learning sklearn conda inria numpy pandas scikit-learn-python training-material visual
Language:Jupyter Notebook 2
Go-MinSeong / Predicting-whether-your-mail-will-be-read
predicting whether you read mail
machine-learning crwaling scikit-learn-python time-series-classification unbalanced-data
Language:Jupyter Notebook 2
RafeyIqbalRahman / Data-Imputation-Techniques
This repository demonstrates data imputation using Scikit-Learn's SimpleImputer, KNNImputer, and IterativeImputer.
imputation imputation-methods scikit-learn scikitlearn-machine-learning scikit-learn-python datapreparation preprocessing sklearn sklearn-library
Language:Python 2
thamizhannal / INSOFE
Works done at International School of Engineering
stock-prediction stock-price-prediction stock-price-forecasting portfolio-optimization simulated-annealing monte-carlo-simulation scikitlearn-machine-learning scikit-learn-python
Language:HTML 2
Aroojzahra908 / Artifical-Intelligence-and-Machine-Learning-
Explore the basics of linear regression, gradient descent, and AI using Python. Get hands-on with NumPy, pandas, Matplotlib, and scikit-learn for practical learning.
artificial-intelligence gradient-descent linear-regression machine-learning matplotlib numpy pandas scikit-learn-python
Language:Jupyter Notebook 1
benjaminweymouth / machine_learning_Risky_Business
Machine Learning Examples: this repo is to show proficiency in building and evaluating several machine learning models to predict credit risk
machine-learning-algorithms credit credit-risk python3 jupyter-notebook scikit-learn scikitlearn-machine-learning scikit-learn-python
Language:Jupyter Notebook 1
dassujan / Breast_Cancer_Detection
🎗️ I have completed this Machine learning Project successfully with 98.24% accuracy which is great for this project. Now, I'm ready to deploy our ML model in the healthcare project. To get more accuracy, I trained all supervised classification algorithms. After training all algorithms, I found that Logistic Regression, Random Forest and XGBoost classifiers are given high accuracy than remain but we have chosen XGBoost.
machine-learning numpy pandas matplotlib-pyplot seaborn-plots data-analysis scikit-learn-python
Language:Jupyter Notebook 1
DNAMAY / ML-Diabetes
Early stage detection of Diabetes risk
diabetes-prediction knn-classifier machine-learning python3 scikit-learn-python supervised-machine-learning
Language:Jupyter Notebook 1
isaccanedo / Machine-Learning-For-Beginners
:star2: 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
data-science education machine-learning machine-learning-algorithms machinelearning machinelearning-python ml python r scikit-learn scikit-learn-python
Language:HTML 1
Jeyjocar / Redes-Neuronales
24/01/2024 Jeyfrey J. Calero R. Aplicación de Redes Neuronales con scikit-learn streamlit, pandas, seaborn y matplolib
app artificial-intelligence programming-language python redes redes-neurais-artificiais streamlit scikit-learn scikit-learn-api scikit-learn-python
Language:Python 1
nafisalawalidris / Machine-Learning-with-Python
Machine Learning with Python final project: Apply ML algorithms to solve real-world problem. Hands-on experience in data preprocessing, model selection, evaluation. Showcase ML proficiency in Python.
classification data-science hierarchical-clustering machine-learning regression scikit-learn-python scipy
Language:Jupyter Notebook 1
NajiAboo / TextClassification
Build custom vacab, Ham /Spam using tfidf , Movie review classification using TFIDF
scikit-learn-python scikit-learn-pipelines movie-review-classifier nlp-machine-learning ham-spam tf-idf countvectorizer nlp-preprocessing custom-vacabulary custom-vacabulary-builder
Language:Jupyter Notebook 1
Shakthi011001 / Sentimental-Analysis-using-Scikit-Learn
Sentiment analysis refers to analyzing an opinion or feelings about something using data like text or images, regarding almost anything. Sentiment analysis helps companies in their decision-making process. For instance, if public sentiment towards a product is not so good, a company may try to modify the product or stop the production. Although there are several known tasks related to sentiment analysis, in this project we will focus on the common binary problem of identifying the positive / negative sentiment that is expressed by a given text toward a specific topic
natural-language-processing scikit-learn-python sentiment-analysis
Language:Jupyter Notebook 1
SubhangiSati / Machine-Learning
This consists of various machine learning algorithms like Linear regression, logistic regression, SVM, Decision tree, kNN etc. This will provide you basic knowledge of Machine learning algorithms using python. You'll learn PyTorch, pandas, numpy, matplotlib, seaborn, and various libraries.
artificial-intelligence classification data-science decision-trees knn-classification knn-model linear-regression logistic-regression machine-learning machine-learning-algorithms python regression-algorithms regression-models scikit-learn-python scikitlearn-machine-learning svm-classifier
Language:Python 1
SURESHBEEKHANI / Python-Libraries-for-Data-Science-and-Machine-Learning
Python's data science and machine learning ecosystem includes NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, and PyTorch. These libraries empower efficient data handling, visualization, and advanced model development.
matplotlib numpy pandas-python pytorch scikit-learn-python sklearn tensorflow tensorflow2
1

scikit-learn-python

microsoft / ML-For-Beginners

dataprofessor / code

mikekeith52 / scalecast

SuperKogito / Voice-based-gender-recognition

flo7up / relataly-public-python-tutorials

dimitrismistriotis / alt-profanity-check

reddyprasade / Machine-Learning-with-Scikit-Learn-Python-3.x

SuperKogito / Voice-based-speaker-identification

BaseMax / ImageRecognitionAI

niitsuma / delayedsparse

Lawrence-Krukrubo / Machine_Learning

UtsavMurarka / MXene-machine-learning

rifatSDAS / satellite_machine_learning

chandakakshat / Digital-Kisaan-Portal

dpscience / DMLLTDetectorPulseDiscriminator

souvikmajumder26 / IEEE-ECG-Ensemble-XGBoost

tanvirnwu / ML-Algorithms--Scikit-learn--Python

AbhishekRS4 / ML_water_potability

AjayZinngg / simple-nltk-chatbot

AMPA-ML-Team / PD-Classification

AshishPandey88 / Nanda-Devi-Glacier-Loss

dblabs-mcgill-mila / machine_learning_with_sklearn

Go-MinSeong / Predicting-whether-your-mail-will-be-read

RafeyIqbalRahman / Data-Imputation-Techniques

thamizhannal / INSOFE

Aroojzahra908 / Artifical-Intelligence-and-Machine-Learning-

benjaminweymouth / machine_learning_Risky_Business

dassujan / Breast_Cancer_Detection

DNAMAY / ML-Diabetes

isaccanedo / Machine-Learning-For-Beginners

Jeyjocar / Redes-Neuronales

nafisalawalidris / Machine-Learning-with-Python

NajiAboo / TextClassification

Shakthi011001 / Sentimental-Analysis-using-Scikit-Learn

SubhangiSati / Machine-Learning

SURESHBEEKHANI / Python-Libraries-for-Data-Science-and-Machine-Learning