This repository is a curated, continuously updated list of free online resources for learning Machine Learning, Deep Learning, NLP, and related fields. It targets ML enthusiasts and practitioners, offering a comprehensive directory of university courses, books, blogs, and research papers to facilitate self-directed learning.

How It Works

The project functions as a structured index of educational materials, categorizing resources by topic (Mathematics, Machine Learning, Deep Learning, NLP, Reinforcement Learning, LLMs, etc.). Each entry includes a title, source (university, company, or individual), and a direct link to the resource, enabling users to easily navigate and access learning content.

Quick Start & Requirements

• Access the repository via its GitHub page.
• No installation or specific software is required; it's a curated list of external links.
• Resources vary in prerequisites; consult individual links for specific requirements (e.g., Python, specific libraries, mathematical background).

Highlighted Details

• Extensive coverage across core ML/DL subfields and emerging areas like LLMs and Quantum ML.
• Links to prominent university courses from institutions like MIT, Stanford, and Harvard.
• Includes a dedicated section for ML in Production and datasets.
• Curated lists of essential books, blogs, webinars, and must-read research papers.

Maintenance & Community

The repository is community-driven, encouraging contributions via pull requests for suggestions and resource additions.

Licensing & Compatibility

The repository itself is licensed under the MIT License, permitting free use and distribution. However, users must adhere to the licensing terms of the individual linked resources.

Limitations & Caveats

This is a directory of links, not a self-contained learning platform. The quality and availability of linked resources are external to this project. Some links may become outdated over time.