Machine Learning My Way - Self-study and Review Guide

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April 13, 2024

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

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