SanjeetS7 / datascience_resources

Data Science Learning (Complete and Comprehensive)

A list of Data Science tutorials with a project based learning approach.

Python:

Web Scraping:

• Mining Twitter Data with Python
• Scrape a Website with Scrapy and MongoDB
• How To Scrape With Python and Selenium WebDriver
• Which Movie Should I Watch using BeautifulSoup

Bots:

• Build a Reddit Bot
• How to Make a Reddit Bot - YouTube (video)
• Build a Facebook Messenger Bot
• Making a Reddit + Facebook Messenger Bot
• How To Create a Telegram Bot Using Python
  • Part 1
  • Part 2
• Create a Twitter Bot In Python

Data Science:

• Learn Python For Data Science by Doing Several Projects (video):
  • Part 1: Introduction
  • Part 2: Twitter Sentiment Analysis
  • Part 3: Recommendation Systems
  • Part 4: Predicting Stock Prices
  • Part 5: Deep Dream in TensorFlow
  • Part 6: Genetic Algorithms

Machine Learning:

• Write Linear Regression From Scratch in Python (video)
• Step-By-Step Machine Learning In Python
• Predict Quality Of Wine
• Solving A Fruits Classification Problem
• Learn Unsupervised Learning with Python
• Build Your Own Neural Net from Scratch in Python
• Linear Regression in Python without sklearn

OpenCV:

• Build A Document Scanner
• Build A Face Detector using OpenCV and Deep Learning
• Build a Face Recognition System using OpenCV, Python and Deep Learning
• Detect The Salient Features in an Image
• Build A Barcode Scanner
• Learn Face Clustering with Python
• Object Tracking with Camshift

Deep Learning:

• Using Convolutional Neural Nets to Detect Facial Keypoints
• Generate an Average Face using Python and OpenCV
• Break A Captcha System using CNNs
• Use pre-trained Inception model to provide image predictions
• Create your first CNN
• Build A Facial Recognition Pipeline
• Build An Image Caption Generator
• Make your Own Face Recognition System
• Train a Language Detection AI in 20 minutes
• Object Detection With Neural Networks
• Learn Twitter Sentiment Analysis -
  • Part I - Data Cleaning
  • Part II - EDA, Data Visualisation
  • Part III - Zipf's Law, Data Visualisation
  • Part IV - Feature Extraction(count vectoriser)
  • Part V - Feature Extraction(Tfidf vectoriser)
  • Part VI - Doc2Vec
  • Part VII - Phrase Modeling + Doc2Vec
  • Part VIII - Dimensionality Reduction
  • Part IX - Neural Nets with Tfdif vectors
  • Part X - Neural Nets with word2vec/doc2vec
  • Part XI - CNN with Word2Vec
• Use Transfer Learning for custom image classification
• Learn to Code a simple Neural Network in 11 lines of Python
• Build a Neural Network using Gradient Descent Approach
• Train a Keras Model To Generate Colors