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84% of students already use AI tools. Only 18% feel prepared to use them
professionally. This curriculum closes that gap.
523 lessons. 20 phases. ~342 hours. Python, TypeScript, Rust, Julia. Every lesson ships
a reusable artifact: a prompt, a skill, an agent, an MCP server. Free, open source, MIT.
You don't just learn AI. You build it. End-to-end. By hand.
Start here: choose what you want to build
You do not need to scan 523 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.
Use every lesson the same way
- Read
docs/en.md and explain the core idea in your own words.
- Type and build the important code instead of treating the code block as decoration.
- Run the lesson command from the repository root, the directory containing
README.md and phases/.
- Keep evidence: the command, working directory, exit code, meaningful output, and the artifact you changed or produced.
- Continue only when you can explain the output and make one small change without guessing.
Commands in lesson pages are paths from the repository root unless the lesson
explicitly says to change directories. If a lesson offers several languages,
run the implementation for the language you are learning.
Clone it and produce your first evidence
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
second 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.
Add the AI tutor in 30 seconds
If Node.js, npx, and a skill-capable coding agent are already installed,
your coding agent can become your tutor in two commands. 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.
Check the local requirements first:
node --version
npx --version
python3 --version
Then install the curriculum skills and choose the host and scope you intend to
use when the installer asks:
npx skills add rohitg00/ai-engineering-from-scratch
Invocation syntax belongs to the host, not to the portable SKILL.md format:
| Host | Start the course | Start Model Context Protocol (MCP) | Start Agent Skills | Run a phase quiz |
|---|
| Codex | start-learning, or choose it from /skills | learn-mcp, or choose it from /skills | learn-agent-skills, or choose it from /skills | check-understanding 13, or choose it from /skills |
| Claude Code | /start-learning | /learn-mcp | /learn-agent-skills | /check-understanding 13 |
| Other compatible hosts | Use start-learning to begin the course. | Use learn-mcp to start the Model Context Protocol (MCP) path. | Use learn-agent-skills to start the Agent Skills Engineering path. | Use check-understanding to quiz me on Phase 13. |
A ten-question placement quiz maps what you already know to a starting phase and
saves a personalized study plan to LEARNING.md. From there, the learn skill
teaches one lesson per session: concept, math, code, quiz. It streams lessons
straight from this repo, and the course-guide skill jumps you to the exact
lesson that covers anything you are stuck on. In Codex, invoke these skills with
learn and course-guide; in Claude Code, use /learn and /course-guide;
in other compatible hosts, ask to use the skill by name.
Only want Model Context Protocol (MCP)? Use the MCP invocation for your host. It creates
MCP-LEARNING.md and follows one 17-lesson route through stateless
requests, transports, bidirectional work, security, reliability, registry
governance, and conformance evidence. The exact order and checkpoints live in
the Model Context Protocol (MCP) manifest.
Only want Agent Skills? Use the Agent Skills invocation for your host. It
creates AGENT-SKILLS-LEARNING.md and follows one coherent five-lesson route:
contract, discovery, invocation, sandbox boundaries, then release evals and
real-host portability. Start on the web with the
Agent Skills path.
The installer lists the hosts it can configure and asks where to install. If
you do not have Node.js, npx, python3, a supported host, or a writable
scope yet, use the website or read docs/en.md manually. That path teaches the