A free roadmap from linear algebra to LLM agents

What it does

This repository is essentially a well-organized syllabus. It collects free resources—courses from Coursera and Google, YouTube playlists, foundational papers like Attention Is All You Need, and tooling guides for LangChain, Ollama, and MCP—into a single reading list covering math, Python, deep learning, LLMs, RAG, agents, and MLOps. Think of it as a bookmark folder that happens to have 5,777 stars.

The interesting bit

The value is in the sequencing. The list starts with linear algebra and probability before jumping to transformers, which is rarer than it should be in AI roadmaps. It also mixes newer topics like Model Context Protocol and agentic coding tools with classic ML frameworks such as Scikit-learn and XGBoost.

Key highlights

• Full-stack coverage: math foundations, Python, deep learning, generative AI, LLMs, prompt engineering, RAG, agents, and deployment.
• Free-only emphasis: Khan Academy, Fast.ai, Google’s ML Crash Course, and multiple Hugging Face courses.
• Canonical papers included: GANs, BERT, GPT-3, and Chain-of-Thought prompting.
• Practical tooling and IDEs: LangChain, LlamaIndex, Ollama, Cursor, and Claude Code.
• Curated books from O’Reilly and Manning, plus YouTube channels like Andrej Karpathy and 3Blue1Brown.

Verdict

Worth bookmarking if you are a developer looking for a structured, zero-cost entry into AI. If you already specialize in production ML or deep learning research, you probably know most of these links.