Read the chapter
Long-form, worked by hand, with the geometry before the algebra. The lecture video sits beside it when one is published.
15 levels · 217 chapters · free and open
Understand AI from first principles.
Not by memorising APIs, but by discovering why each idea had to exist. Every chapter answers the question the previous one left open — and you move on when you can show you understood it.
1 of 217 chapters open · new chapters as they are written and filmed
Do the practice
Six graded levels per chapter, from explain-it to investigate-it, plus a Python lab that runs in your browser.
Pass the test
A short multiple-choice test on the chapter you just read. Score 60% and the next chapter opens.
The path
levels open in order · 60% on a chapter test opens the next Level 00
What does learning mean
Level 01
Linear Algebra
Level 02
Mathematics of Change
Level 03
Probability and Uncertainty
Level 04
Classical Machine Learning
Level 05
Doing Machine Learning Honestly
Level 06
Neural Networks from First Principles
Level 07
Training Deep Networks
Level 08
Computer Vision
Level 09
Sequences Language and Transformers
Level 10
Generative AI and Foundation Models
Level 11
Retrieval Tools and Agents
Level 12
Reinforcement Learning
Level 13
Research Literacy
Level 14
Production AI and MLOps
Stanford-level depth, without the prerequisite maze
Vectors appear when we need to describe several features at once. Derivatives appear when we need to know how a prediction changes. Nothing is taught before the problem that demands it.
Open, and free to read
The curriculum is a public repository and the videos are on YouTube. No paywall, no subscription, and the pipeline that builds the lectures is open source too.