yanshengjia/ml-road

What it is:

ml-road is a curated list of learning resources for machine learning and agentic AI. It's a single place where the author has collected links to courses, books, and papers, organized by topic.

What it contains:

• Courses: A table of university and online courses (e.g., from Stanford, Coursera, NTU) covering machine learning, deep learning, NLP, and reinforcement learning. Each entry lists the institution, lecturer, and links to video lectures (YouTube, Bilibili, etc.) and course homepages.
• Books: A table of well-known textbooks and practical guides (e.g., 'Deep Learning' by Goodfellow, 'Pattern Recognition and Machine Learning' by Bishop). Many entries include direct PDF links stored in the repo's resources folder, plus some Chinese translations.
• Agentic AI: A newer section with courses, SDKs, and research papers focused on AI agents, LLM applications, and prompt engineering (e.g., Andrew Ng's short courses, the ReAct paper).

Important caveats:

• The README includes a disclaimer stating that all resources are for educational purposes only and must not be used commercially. It also asks authors of any included ebooks to contact the repo owner if they believe their intellectual property is violated, promising prompt removal.
• The project is a list of links and PDFs, not a software library or framework. It's a study guide, not a tool you run.

In short: If you want a hand-picked roadmap of high-quality ML and AI learning material, this repo is a convenient index — but be aware of the copyright restrictions stated in its own disclaimer.