Markdown roadmaps that trace AI learning from math to MLOps

What it does

This repository is a curated index of books, university courses, YouTube playlists, and research papers organized into thematic markdown roadmaps. It covers foundations—linear algebra, calculus, Python—and branches into machine learning, deep learning, computer vision, NLP, large language models, generative AI, RAG, MLOps, and AI safety. The roadmaps are designed to be followed sequentially or mixed based on a learner’s goals.

The interesting bit

Instead of dumping links, the project treats learning like engineering with career-oriented paths—Data Scientist, GenAI Engineer, Research Scientist—that suggest explicit module sequences. The math section is unusually granular, mapping specific concepts like eigenvalues and convex optimization to exact textbooks and lecture series.

Key highlights

• Roadmaps span beginner programming to PhD-level research and AI safety alignment
• Career tracks map explicit learning sequences to roles like MLOps Engineer and NLP Engineer
• Math resources are graded by difficulty and paired to specific AI use cases (e.g., SVD for embeddings, backpropagation for multivariable calculus)
• Includes emerging topics like LLMs, RAG, and generative AI alongside classical machine learning
• All instructional content is external; the repository provides structure and curation, not original courses

Caveats

• This is an aggregator, not a course: all books, videos, and papers live behind external links
• The repository’s value depends on ongoing curation and the stability of those third-party resources

Verdict

Self-directed learners who want a syllabus-level view of AI will find this more useful than another notebook tutorial. If you need runnable code or original instruction, look elsewhere—it is a map, not a vehicle.