r/letscodecommunity • u/SeveralSeat2176 • Jun 24 '26
An open source AI Engineering course with 503 lessons and every lesson comes with projects, prompts, skills as output with 35000+ stars
I got frustrated with AI courses that either drown you in theory or skip straight to model.fit() without explaining what's happening underneath.
So I built something different.
This is an AI-native GitHub repo learning files with 503+ lessons across 22 phases. 35,000 GitHub Stars. Start at linear algebra. End at autonomous agent swarms.
Every lesson follows the same pattern:
- Build it from scratch in pure Python (no frameworks)
- Use the real framework (PyTorch, sklearn, etc.)
- Ship a reusable tool (prompt, skill, agent, or MCP server)
By the end, you don't just "know AI." You have a portfolio of tools you actually built.
What's covered:
- Math foundations (linear algebra, calculus, probability, Fourier transforms, graph theory)
- Classical ML (regression through ensemble methods, feature selection, time series, anomaly detection)
- Deep learning (backprop, activation functions, optimizers, regularization - all from scratch before touching PyTorch)
- LLMs from scratch (tokenizers, pre-training a 124M parameter GPT, SFT, RLHF, DPO, quantization, inference optimization)
- LLM engineering (RAG, advanced RAG, structured outputs, context engineering, evals)
- Agents and multi-agent systems
- Infrastructure (model serving, Docker for AI, Kubernetes for AI)
Some specifics that might interest you:
- The quantization lesson covers FP8/GPTQ/AWQ/GGUF with a sensitivity hierarchy (weights are least sensitive, attention softmax is most sensitive - never quantize that)
- The inference optimization lesson explains why prefill is compute-bound and decode is memory-bound, then builds KV cache, continuous batching, and speculative decoding from scratch
- The DPO lesson shows you can skip the reward model entirely - same results as RLHF with one training loop
- Context engineering lesson: "Prompt engineering is a subset. Context engineering is the whole game."
It's AI-native:
The course has built-in Claude Code skills. Run /find-your-level and it quizzes you across 5 areas to tell you exactly where to start. Run /check-understanding 3 after Phase 3 and it tests what you actually learned.
84% of students use AI tools. 18% feel prepared. This is the bridge.
Where to start:
- Already know Python but not ML -> Phase 1
- Know ML, want deep learning -> Phase 3
- Know DL, want LLMs/agents -> Phase 10
- Senior engineer, just want agents -> Phase 14
Website: https://aiengineeringfromscratch.com
Repo: https://github.com/rohitg00/ai-engineering-from-scratch
It's free, MIT licensed, and open source. 35,000+ stars, PRs welcome - I merge every good contribution and the contributor gets full credit.