AI Engineering from Scratch

rohitg00’s hands-on AI/ML engineering curriculum — 503 lessons across 20 phases, ~33k stars. Parked in the hub _inbox on 2026-06-15 under learning-roadmaps; ingested here on the 2026-08-04 spin-out.

The arc

Mathematics (linear algebra, calculus, probability) → classical ML → deep learning (neural networks from scratch, then PyTorch/JAX) → computer vision, NLP, speech, RL → LLM engineering (build-from-scratch, tokenization, fine-tuning, RLHF, quantization) → production (RAG, agents, MCP servers, infrastructure) → safety and alignment. 17 capstones.

Why it is the cluster’s outlier

The other two founding sources are meta-curricula pointing at other people’s material and shaped by an exam. This one is neither.

Its pedagogy is stated and distinctive: build-it → use-it → ship-it — implement the thing by hand, then use the framework equivalent, then produce a reusable artifact. “You don’t just learn AI, you build it by hand.” The output is a portfolio, not readiness to be questioned.

And there is no standardized AI-engineering interview to prepare for. The absence of an assessment regime and the presence of construction-based pedagogy arrive together here, which is the observation the spoke’s synthesis is built on — and, on three sources, an observation rather than a finding.

Where it stops being this spoke’s business

The subject matter belongs to ../machine-learning-wiki, which owns machine learning as a discipline. This page is about the curriculum — its sequence, its pedagogy, its scope. A source teaching a technique to make a claim about that technique routes there instead.

Tier

T3 — the author’s own repository. 503 lessons is a stated count, not a verified one, and there is no evidence here about whether anyone completes them.

Related

meta-curriculum · coding-interview-university · awesome-system-design-resources