AI Engineering from Scratch（英文原版）

Read in your language:Türkçe
_{Translated landing pages, committed to the repo. English is canonical; lesson pages are machine-translated on the translations branch. See docs/i18n.md.}

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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.

Your goal	Learn on GitHub	Learn on the website
I am new and want the complete foundation	Phase 0: Setup and Tooling	Dev Environment
I know Python and want math plus ML foundations	Phase 1: Math Foundations	Linear Algebra Intuition
I want to build production LLM applications	Phase 11: LLM Engineering	Prompt Engineering
I want to build agents	Phase 14: Agent Engineering	The Agent Loop
I want to use coding agents on real repositories	Agent-Assisted Engineering path	Agent-Assisted Engineering
I want to shape the right build before implementation	Product Judgment and Delivery path	Product Judgment and Delivery
I want to build with Model Context Protocol (MCP)	Model Context Protocol (MCP) route	Model Context Protocol (MCP) path
I want to write and ship Agent Skills	Focused Agent Skills route	Agent Skills path
I want to prepare for a Claude certification	Certification onboarding	Certification Academy

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

1. Read docs/en.md and explain the core idea in your own words.
2. Type and build the important code instead of treating the code block as decoration.
3. Run the lesson command from the repository root, the directory containing README.md and phases/.
4. Keep evidence: the command, working directory, exit code, meaningful output, and the artifact you changed or produced.
5. 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 concepts, but real-host discovery, invocation, script, and uninstall evidence remains pending until the preflight is available. Read the lessons at aiengineeringfromscratch.com.

How this works

Most AI material teaches in scattered pieces. A paper here, a fine-tuning post there, a flashy agent demo somewhere else. The pieces rarely line up. You ship a chatbot but can't explain its loss curve. You hook a function to an agent but can't say what attention does inside the model that's calling it.

This curriculum is the spine. 20 phases, 523 lessons, four languages: Python, TypeScript, Rust, Julia. Linear algebra at one end, autonomous swarms at the other. Every algorithm gets built from raw math first. Backprop. Tokenizer. Attention. Agent loop. By the time PyTorch shows up, you already know what it's doing under the hood.

Each lesson runs the same loop: read the problem, derive the math, write the code, run the test, keep the artifact. No five-minute videos, no copy-paste deploys, no hand-holding. Free, open source, and built to run on your own laptop.

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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

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The shape of a lesson

Each lesson lives in its own folder, with the same structure across the entire curriculum: