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
- Khan Academy - Fundamental Math courses.
- Introduction to computational thinking for Data Science - MIT - Playlist.
- Mathematics for Machine Learning - Deisenroth et al - Book.
- Essence of Linear Algebra - 3Blue1Brown - Playlist.
- Gilbert Strang lectures on Linear Algebra - MIT - Playlist.
- Introduction to Applied Linear Algebra - Boyd & Vandenberghe - Book.
- Essence of Calculus - 3Blue1Brown - Playlist.
- The Matrix Calculus You Need For Deep Learning - Explained.ai - Article.
- 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.
- 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.
- fasteR: fast lane to learning R! - Norman Matloff - Course.
- R for Data Science - Hadley Wickham - Book.
- 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.
- MongoDB Basics - Course.
- The Git Parable - Article.
- Learn Git branching - Interactive tutorial.
- Git Internals - Learn by building your own Git - Interactive tutorial.
- AWS Cloud Practitioner Essentials - Course.
- 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.
- 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.
- To do
- Neural Networks - 3Blue1Brown - Playlist.
- Neural Networks and Deep Learning - Michael Nielsen - Book.
- Deep Learning from the Foundations - fast.ai - Course.
- 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.
- 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.
Newsletters:
- Data Elixir: stay up to date in Data Science.
- The Batch: what matters in AI right now.
- Alpha Signal: the best of Machine Learning. Summarized by AI.
Stranger things:
- Papers with code: reproducible research. Yay!
- ArXiv Sanity Preserver: keep your sanity while sifting through ArXiv. The 'top hype' tab is pretty cool.
- Artificial Intelligence Podcast: nice conversations about AI.