rohitg00/ai-engineering-from-scratch

Implement model internals, retrieval pipelines, and agent runtimes. Test them, inspect failures, and keep the code and evaluation results.

Start learning · Choose a path · Try a lab · Build a project · Browse the curriculum

Free, open source, MIT. Learn on the website, with a coding agent, or by running local code.

542 lessons. 20 phases. Python, TypeScript, Rust, Julia.

Sponsors

Your support keeps every lesson free and open source. See all supporters · Become a sponsor

Learning paths

Compare career paths · Prerequisites and study time

Gradient descent

Twenty starting points follow gradient descent on a quadratic loss. The graph shows their positions and mean loss after each update.

Adjust the learning rate in the lesson · Compare GD, momentum, and Adam in code

Projects

Three projects with staged starters, reference implementations, and local graders. Run commands from the repository root after setup. Starters fail until you implement the stages.

Browse all projects · Career practice guide

Choose how to learn

On the website

Open any completed lesson on aiengineeringfromscratch.com or expand a phase under Contents. No setup, no cloning.

With an AI tutor

If Node.js, npx, and a skill-capable coding agent are already installed, your coding agent can become your tutor. A repository clone is not needed to install or read the tutor. Runnable focused-path labs need python3. Agent Skills host labs also need a selected host and a writable user or project skill scope.

npx skills add rohitg00/ai-engineering-from-scratch

Choose the host and scope when the installer asks. Use start-learning in Codex, /start-learning in Claude Code, or ask your host to use the skill by name.

Run local code

git clone https://github.com/rohitg00/ai-engineering-from-scratch.git
cd ai-engineering-from-scratch
python3 phases/00-setup-and-tooling/01-dev-environment/code/verify.py --route beginner
python3 phases/01-math-foundations/01-linear-algebra-intuition/code/vectors.py

The preflight separates requirements needed now from tools needed later. Every required failure includes the detected reason and a corrective command. The vectors.py command is a dependency-free lesson and ends by showing that a matrix times a vector is the operation inside a neural network layer. Save that terminal output as your first evidence.

Choose a learning path

You do not need to scan 542 lessons before beginning. Pick one goal. Each link opens the same curriculum on GitHub or the website, and both versions use the same lesson code.

Not sure where you fit? Use the start-learning placement tutor or the website prerequisites guide.

Compare four core domains and six career routes in the AI Engineering Learning Paths.

The shape of the curriculum

Twenty phases stack on top of each other. Math is the floor. Agents and production are the roof. Skip ahead if you already know the lower layers, but don’t skip and then wonder why something at the top is breaking.

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flowchart TB
  P0["Phase 0 — Setup & Tooling"] --> P1["Phase 1 — Math Foundations"]
  P1 --> P2["Phase 2 — ML Fundamentals"]
  P2 --> P3["Phase 3 — Deep Learning Core"]
  P3 --> P4["Phase 4 — Vision"]
  P3 --> P5["Phase 5 — NLP"]
  P3 --> P6["Phase 6 — Speech & Audio"]
  P3 --> P9["Phase 9 — RL"]
  P5 --> P7["Phase 7 — Transformers"]
  P7 --> P8["Phase 8 — GenAI"]
  P7 --> P10["Phase 10 — LLMs from Scratch"]
  P10 --> P11["Phase 11 — LLM Engineering"]
  P10 --> P12["Phase 12 — Multimodal"]
  P11 --> P13["Phase 13 — Tools & Protocols"]
  P13 --> P14["Phase 14 — Agent Engineering"]
  P14 --> P15["Phase 15 — Autonomous Systems"]
  P15 --> P16["Phase 16 — Multi-Agent & Swarms"]
  P14 --> P17["Phase 17 — Infrastructure & Production"]
  P15 --> P18["Phase 18 — Ethics & Alignment"]
  P16 --> P19["Phase 19 — Capstone Projects"]
  P17 --> P19
  P18 --> P19

Contents

Twenty phases. Click any phase to expand its lesson list.

Phase 0: Setup & Tooling 13 lessons

Get your environment ready for everything that follows.

Books and certifications

The toolkit

Every lesson produces a reusable artifact. Install one in your agent or use the scripts below from the repository root.

Contributing

Before submitting a lesson, run the invariant check:

python3 scripts/audit_lessons.py           # full curriculum
python3 scripts/audit_lessons.py --phase 14  # single phase
python3 scripts/audit_lessons.py --json    # CI-friendly output

Exit code is non-zero when any rule fails. Rules (L001–L010) validate directory shape, docs/en.md presence + H1, code/ non-emptiness, quiz.json schema (rejects the legacy q/choices/answer keys that caused issue #102), and relative links inside lesson docs.

Sponsor the work

114,584 readers  ·  181,995 page views in the last 30 days  ·  as of 2026-08-29

Free, MIT-licensed, 542 lessons. Thank you to the sponsors and backers who make the work possible. See all sponsors and backers.

Want to support the work? See sponsorship options, including hardware sponsorships, or sponsor on GitHub.

If this manual helped you, star the repo. It keeps the project alive.

License

MIT. Use it however you want — fork it, teach it, sell it, ship it. Attribution appreciated, not required.

Maintained by Rohit Ghumare and the community.

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