Math Bootcamp

From Algebra to Probability — A practical guide for ML

Preface

Welcome to Math Bootcamp — a practical guide to the mathematics behind machine learning, written as a self-contained book.

The book is structured around the following flow for each topic:

1. Intuition — what is the idea, and why does it matter?
2. Math — the formal definitions and derivations.
3. Code — Python implementations you can run yourself.
4. Visualization — diagrams and plots that make the math concrete.

This is a living document. New chapters are added as they are written. If you spot an error or have a suggestion, the Edit this page link in the top-right of each chapter goes straight to the source on GitHub.

Note

This is a proof-of-concept build. Only the introductory chapter is currently available on the web; the remaining chapters are being migrated from the LaTeX source.