A curated self-study roadmap through Stanford, CMU, and MIT courses. Track progress, schedule your week, and navigate the path from zero to deep learning practitioner.

An interactive map of the math and programming foundations you need before diving into ML. Linear algebra, calculus, probability, Python — with the best resources for each.

Build the systems you use every day, from scratch. An OS kernel, a TCP/IP stack, a database engine, and a distributed key-value store — MIT, CMU, and Stanford's project-first systems courses.