README.md

Machine Learning / Data Science Resources

Here is a collection of the best blogs, youtube, tweets and github repos in the field of Machine Learning, Deep Learning, Web Scrapping, Web applications, Chatbots and Mathematics.

Web Scraping:

• Web Scraping and Parsing HTML in Python with Beautiful Soup
• 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

Web Applications:

• beginer-friendly Django tutorial
• Build a Microblog with Flask
• Create a Blog Web App In Django
  • Part I : Introduction
  • Part II : Extension To Add More Features
• Choose Your Own Adventure Presentations
• Build a Todo List with Flask and RethinkDB
• Build a Todo List with Django and Test-Driven Development
• Build a RESTful Microservice in Python
• Microservices with Docker, Flask, and React
• Build A Simple Web App With Flask
• Build a RESTful API with Flask – The TDD Way

Bots:

• Build a Reddit Bot
• How to Make a Reddit Bot - YouTube
• 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:
  • 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
• 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

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

Twitter threads by Prashant

Machine Learning and Deep Learning

• Starting with Machine Learning
• Writing Research Paper
• Autoencoders
• Knowledge Distillation
• Spiral Classification
• Linear And Non Linear Transformations
• Style Transfer
• Simulated Annealing
• Learning Rate and Log Scale
• Overfitting High Accuracy
• Move on from Basic Problems
• MNIST in 3D
• Time-Series Models
• Lessons from a Data Problem
• Precision and Recall
• Ensemble Techniques
• Convolutions 1D 2D 3D
• Calculating Convolution Shapes
• Backpropagation Intuition
• PCA on Fashion MNIST
• Interpretation and Flexibility
• Irreducible Error
• Embedding Preprocessing in Model
• YOLO Object Detection
• Neural Nets as Combination of Linear Methods
• Validation vs Test Set
• R2 in Regression explained
• Regressions in 3D
• Residual Analysis
• Validation vs Test Set
• Apriori
• Multicollinearity
• Z-Score Explanation
• Segmentation Performance Measure
• Simple Spam Classifier
• Image Similarity Intuition
• Communication in Data Science
• Survival Bias Fallacy
• Survival Bias Story
• Feature Scaling
• Deployed Model Drift
• Dropout
• Logic Gates
• Feature Selection Elimination
• KMeans Clustering
• Feature Selection Types
• Normalization vs Standardization
• Machine Learning Jargon Part1
• Machine Learning Jargon Part2
• Computer Vision Skills
• One-Hot Encoding

Python

• Keras Custom Generator
• Python Logger
• Sub List Operations
• Feature Importance
• Split and Join Keras Models
• Shuffle Arrays Synchronously
• Operations on Dates
• Splitting Joining Models
• Multiple Variable Assignment
• Ipython Cell Timing
• Decision Forests in Tensorflow
• Python Ellipsis
• Python Glob
• Set Operation for Data Curation
• Or as Short Circuit Operator
• Tensorflow Callbacks
• Zen of Python
• Existing Directories
• Eight Pandas Functions
• Function Annotation
• Corey Schafer Learning Resources
• Dict from Lists
• Reverse Dict
• System Command Run
• Confidence Interval Map
• Eval in Python
• Sorting Dictionaries
• Saving and Loading Numpy Arrays
• Raising Exceptions
• Args and Kwargs
• Useful Numpy Functions
• Joins in Pandas
• Merging Dictionaries

Random Tips and Depictions

• Machine Learning Overview
• Machine Learning Algorithms Map
• Skills FOr Data Science
• Confusion Matrix
• Precision Vs Recall
• Precision Vs Recall Handdrawn
• Visualization Cheatsheet
• Accuracy Vs Precision
• Python Versatility
• Amazon Forecast
• Normal Distribution Summary
• Data Science Pyramid
• Decision Boundaries
• Statistics Concept Map
• Common Machine Learning Steps
• Neural Network Recognizes Dog
• Supervised Learning Flow