0.5.5 Core machine learning and deep learning textbooks listed in MLMW (all free).
Core machine learning and deep learning textbooks listed in MLMW, also several additions to various sections:
Big Ideas: Natural Language Processing with MacArthur Fellow Dan Jurafsky(interview, beginner-friendly) and Lena Voita NLP Course | For You
scikit-learn: machine learning in Python by Gael Varoquaux. Part of Scientific Python Lectures. One document to learn numerics, science, and data with Python. EP: I think an underappreciated resource (beginner, but programming knowledge required).
Andrew Ng lecture notes on machine learning from a 2022 course. EP: Andrew Ng best known for a deep learning course, but the classic machine learning notes are very well structured (intermediate).
Deisenroth et al. (2020). Mathematics for Machine Learning. Chapter 8 “When Models Meet Data” is an accessible introduction to statistical learning (very beginner friendly).
Shawe-Taylor (2023). Statistical Learning Theory for Modern Machine Learning, has video and slides (advanced).
Causal ML Book by Chernozhukov et al. (2024) (advanced).
Link to MLMW 0.5.5: https://docs.google.com/document/d/e/2PACX-1vT9ZkQJDDimZuPgBb7_hUJ40lm8LhqzL45HwIcYRYHw0AQkwA7pcqg0AIE7Gwf3QpAnZ34-BrFrWovO/pub