One Repo, Twelve Weeks, Mostly Other People's Videos

What it does This repository is a curated syllabus for Siraj Raval’s “Learn Machine Learning in 3 Months” YouTube video. It maps a 12-week calendar onto free external resources: Month one is foundational math through MIT OpenCourseWare, YouTube playlists, and edX courses in linear algebra, calculus, probability, and algorithms. Month two adds Python data-science tutorials, TensorFlow introductions, a Udacity machine-learning course, and a link to project ideas. Month three shifts to deep learning via more YouTube playlists, Fast.AI, and a capstone assignment to re-implement Raval’s own GitHub repositories.

The interesting bit There is no code or original coursework here—only outbound links. The project’s value is entirely in the sequencing, treating a full semester of math as a four-week boot camp before you touch a framework. The capstone is admirably direct: “go rebuild my other repos.” It is essentially a human-curated RSS feed for the impatient autodidact.

Key highlights

• Structured as a 12-week calendar from linear algebra to neural networks
• Sources are free external material: MIT OCW, edX, YouTube, Udacity, and Fast.AI
• Explicit math and algorithms weeks before any machine-learning coding
• Culminates in re-implementing Raval’s existing GitHub repositories
• Includes a Slack channel and Twitter follow list for community support

Verdict Grab this if you want a rigid, opinionated study schedule and don’t mind that every resource lives outside the repo. Skip it if you are looking for original exercises, code, or a gently paced introduction—you will find neither here.