A semester of Stanford ML, distilled to PDFs

What it does This repository hosts PDF cheat sheets covering the core topics of Stanford’s CS 229 Machine Learning course: supervised learning, unsupervised learning, deep learning, and practical training tips. It also includes refresher sheets for the prerequisite math—probability, statistics, algebra, and calculus—and bundles everything into a single “Super VIP Cheatsheet.” The content is available in eleven languages.

The interesting bit There is no code here, just densely packed theory distilled by an MIT alum and a Stanford alum into something resembling pocket-sized review cards. The project also runs a dedicated translation repo, which is unusual for a static-notes project.

Key highlights

• Four topic cheatsheets: Supervised Learning, Unsupervised Learning, Deep Learning, and Tips & Tricks.
• Two math refresher PDFs: Probabilities & Statistics, Algebra & Calculus.
• A “Super VIP Cheatsheet” that merges all concepts into one reference document.
• Community translations into 11 languages.
• A companion website mirrors the content for mobile reading.

Verdict Reach for this if you need a quick theoretical refresher before an interview or exam. Look elsewhere if you want runnable code or implementation tutorials; this is purely reference material.