This repository curates free Machine Learning and Artificial Intelligence university courses from top institutions like MIT, Stanford, and Berkeley. It serves as a centralized, searchable catalog for students, researchers, and practitioners seeking structured learning materials in these fields.

How It Works

The project acts as a curated index, compiling publicly available course information, lecture videos, and syllabi from leading universities. It organizes these resources by institution, course number, and topic, providing direct links to the original materials for easy access and learning.

Quick Start & Requirements

No installation or specific requirements are needed beyond a web browser to access the linked course materials.

Highlighted Details

• Comprehensive listing of courses from MIT, Stanford, Berkeley, CMU, NYU, and others.
• Includes lectures from prominent figures like Andrew Ng, Lex Fridman, and Yann LeCun.
• Covers a wide range of ML/AI topics including Deep Learning, NLP, Reinforcement Learning, and foundational concepts.
• Provides links to official course pages, YouTube lectures, and sometimes notes or syllabi.

Maintenance & Community

This project appears to be a static compilation of links. No specific community channels or active maintenance contributors are mentioned in the README.

Licensing & Compatibility

The licensing of the individual course materials is determined by the respective universities and platforms (e.g., YouTube, MIT OpenCourseWare). This repository itself is likely under a permissive license, but users must adhere to the terms of service for each linked external resource.

Limitations & Caveats

The repository is a curated list and does not host any course content itself. The availability and format of the linked materials are subject to change by the original providers. The last update year for some courses is not explicitly stated, potentially indicating outdated content for rapidly evolving fields.