A zero-to-hero AI curriculum built from free internet courses

What it does This repository is a curated index of free learning materials—mostly YouTube playlists, MOOCs, and blog posts—arranged into modules zero through ten plus a bonus section. It starts with Python setup and linear algebra, then moves through data science, machine learning, computer vision, deep learning, generative AI, NLP, reinforcement learning, and agentic AI. The maintainer marks favored content with a star. It is, in essence, a syllabus where every assignment is a hyperlink.

The interesting bit The value is entirely in the sequencing. The author tries to close the gap between “I want to learn AI” and “which of these four hundred free videos are actually worth my time?” by ranking modules by difficulty and mixing foundational Harvard and MIT courses with crash-course playlists.

Key highlights

• Covers the full arc: from pip and matrices to RAG, GANs, and agentic AI
• Blends heavyweight academic courses (MIT Linear Algebra, Andrew Ng’s ML Specialization) with four-hour YouTube binges
• Includes a projects section and auxiliary reading lists (newsletters, blogs)
• Starred (⭐) recommendations flag the maintainer’s picks among the noise
• Explicitly allows parallel or sequential study

Caveats

• It is essentially a well-organized bookmark collection; there is no original code, exercises, or interactive content here
• Link rot is inevitable; there is no stated process for keeping external resources current
• Some module descriptions reuse the same enthusiastic boilerplate (“vast deep ocean”) or exclamations (“THAT’S SO COOL!”)

Verdict Grab this if you are a self-learner who needs a structured path through the free AI education ecosystem and doesn’t mind that the “product” is a list of other people’s products. Skip it if you are looking for a hands-on framework, original tutorials, or anything you can run locally.