Machine Learning Journey

This repository contains a curated collection of free learning resources on topics related to Machine Learning. I tend to favor materials that explain concepts visually and promote a hands-on learning experience. The references are organized as follows:

• Math & Stats
  • Fundamentals
  • Linear Algebra
  • Calculus
  • Statistics
• Computing
  • Python
  • R
  • SQL
  • NoSQL
  • Git
  • Cloud
• Machine Learning
  • Fundamentals
  • Data-specific resources
  • Model-specific resources
  • Application-specific resources
  • Digging deeper

Math & Stats

Fundamentals

• Khan Academy - Fundamental Math courses.
• Introduction to computational thinking for Data Science - MIT - Playlist.
• Mathematics for Machine Learning - Deisenroth et al - Book.

Linear Algebra

• Essence of Linear Algebra - 3Blue1Brown - Playlist.
• Gilbert Strang lectures on Linear Algebra - MIT - Playlist.
• Introduction to Applied Linear Algebra - Boyd & Vandenberghe - Book.

Calculus

• Essence of Calculus - 3Blue1Brown - Playlist.
• The Matrix Calculus You Need For Deep Learning - Explained.ai - Article.

Statistics

• Seeing Theory - Brown University - Interactive book.
• Probability Cheatsheet - William Chen - Cheatsheet.
• Probabilistic Programming & Bayesian Methods for Hackers - Davidson-Pilon - Book.
• Computer Age Statistical Inference - Efron & Hastie - Book.

Computing

Python

• Learn Python: free interactive python tutorial - Interactive tutorial.
• Python programming: learn python in one video - Derek Banas - Live coding.
• Python Magical Universe - Anna-Lena Popkes - Interactive course.
• Reproducible Data Analysis in Jupyter - Jake VanderPlas - Playlist.
• Microsoft Learn for NASA - Tutorials.

R

• fasteR: fast lane to learning R! - Norman Matloff - Course.
• R for Data Science - Hadley Wickham - Book.

SQL

• Intro to SQL: Learn SQL for working with databases, using Google BigQuery to scale to massive datasets - Kaggle - Interactive tutorial.
• Advanced SQL: Take your SQL skills to the next level - Kaggle - Interactive tutorial.
• The SQL Tutorial for Data Analysis - Mode - Tutorial.

NoSQL

• MongoDB Basics - Course.

Git

• The Git Parable - Article.
• Learn Git branching - Interactive tutorial.
• Git Internals - Learn by building your own Git - Interactive tutorial.

Cloud

• AWS Cloud Practitioner Essentials - Course.

Machine Learning

Fundamentals

• Artificial Intelligence: a concise conceptual introduction - TDS - Article.
• A visual introduction to machine learning I - R2D3 - Article.
• A visual introduction to machine learning II - R2D3 - Article.
• Making Friends with Machine Learning - Cassie Kozyrkov - Course.
• Machine Learning Crash Course - Google - Course.
• Machine Learning for Beginners - Microsoft - Course.
• Introduction to Machine Learning for Coders - fast.ai - Course.
• Practical Deep Learning for Coders - fast.ai - Course.
• The Elements of Statistical Learning - Hastie et al - Book.

Data-specific resources

Natural Language Processing

• Natural Language Processing: Distinguish yourself by learning to work with text data - Kaggle - Interactive tutorial.
• A Code-First Introduction to Natural Language Processing - fast.ai - Course.
• Text as Data - Chris Bail - Course.

Computer vision

• To do

Time series

• Forecasting: Principles and Practice - Hyndman & Athanasopoulos - Book.

Model-specific resources

Neural networks

• Neural Networks - 3Blue1Brown - Playlist.
• Neural Networks and Deep Learning - Michael Nielsen - Book.
• Deep Learning from the Foundations - fast.ai - Course.

Application-specific resources

Recommender systems

• How does Netflix recommend movies? Matrix Factorization - Luis Serrano - Video.

Digging deeper

Feature engineering

• Feature Engineering and Selection: A Practical Approach for Predictive Models - Max Khun - Book.
• Feature Engineering: Discover the most effective way to improve your models - Kaggle - Interactive tutorial.

Interpretable Machine Learning

• Machine Learning Explainability: Extract human-understandable insights from any machine learning model - Kaggle - Interactive tutorial.
• Interpretable Machine Learning: A Guide for Making Black Box Models Explainable - Christoph Molnar - Book.

Other cool resources

• Papers with code: reproducible research. Yay!
• ArXiv Sanity Preserver: keep your sanity while sifting through ArXiv. The 'top hype' tab is pretty cool.
• Data Elixir: great Data Science newsletter.
• The Batch: a newsletter from Andrew Ng!
• Artificial Intelligence Podcast: nice conversations about AI.