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Translated landing pages, committed to the repo. English is canonical; lesson pages are machine-translated on the translations branch. See docs/i18n.md.
From the creator of Agent Memory - #1 Persistent memory ⭐ 
which naturally works with any agents or chat assistants.
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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.
114,584 readers · 181,995 page views in the last 30 days · as of 2026-08-29
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.
Thank you to our sponsors.
Your support keeps every lesson free and open source.
See all supporters
Become a sponsor
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
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.
%%{init: {'theme':'base','themeVariables':{'primaryColor':'#fafaf5','primaryTextColor':'#1a1a1a','primaryBorderColor':'#3553ff','lineColor':'#3553ff','fontFamily':'JetBrains Mono','fontSize':'12px'}}}%%
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:
phases/<NN>-<phase-name>/<NN>-<lesson-name>/
├── code/ runnable implementations (Python, TypeScript, Rust, Julia)
├── docs/
│ └── en.md lesson narrative
└── outputs/ prompts, skills, agents, or MCP servers this lesson produces
Every lesson follows six beats. The Build It / Use It split is the spine — you implement the
algorithm from scratch first, then run the same thing through the production library. You
understand what the framework is doing because you wrote the smaller version yourself.
%%{init: {'theme':'base','themeVariables':{'primaryColor':'#fafaf5','primaryTextColor':'#1a1a1a','primaryBorderColor':'#3553ff','lineColor':'#3553ff','fontFamily':'JetBrains Mono','fontSize':'13px'}}}%%
flowchart LR
M["MOTTO<br/><sub>one-line core idea</sub>"] --> Pr["PROBLEM<br/><sub>concrete pain</sub>"]
Pr --> C["CONCEPT<br/><sub>diagrams & intuition</sub>"]
C --> B["BUILD IT<br/><sub>raw math, no frameworks</sub>"]
B --> U["USE IT<br/><sub>same thing in PyTorch / sklearn</sub>"]
U --> S["SHIP IT<br/><sub>prompt · skill · agent · MCP</sub>"]
Getting started
Three ways in. Pick one.
*Option A — learn in your terminal (recommended).* After the Node.js,
npx, host, and scope preflight above, install the learning skills into a
compatible agent and let the course drive itself:
npx skills add rohitg00/ai-engineering-from-scratch
Use the host-specific invocation table above. The installed skills provide
start-learning, learn, course-guide, and the focused
learn-mcp and learn-agent-skills routes. Lesson prose can
stream from this repository without a clone. A local clone is required for
copied repository code commands and executable MCP or Agent Skills labs.
Progress lives in LEARNING.md, MCP-LEARNING.md, or
AGENT-SKILLS-LEARNING.md in your project, so every session can resume.
Option B — read. Open any completed lesson on
aiengineeringfromscratch.com or expand a phase under
Contents. No setup, no cloning.
Option C — clone and run.
git clone https://github.com/rohitg00/ai-engineering-from-scratch.git
cd ai-engineering-from-scratch
python3 phases/01-math-foundations/01-linear-algebra-intuition/code/vectors.py
Cloning also auto-loads the learning skills in Claude Code, and gives every
lesson's code to the learn tutor for real execution instead of read-along.
Prerequisites
- You can write code (any language; Python helps).
- You want to understand how AI actually works, not just call APIs.
Prepare for Claude certifications
The Claude Certification Academy is a free,
open-source preparation program for all four official Claude certification tracks:
Associate Foundations, Developer Foundations, Architect Foundations, and Architect
Professional. Each route combines blueprint-mapped lessons, runnable labs, a
diagnostic, capstone work, and a full-length original practice exam.
Use the AI-native GitHub onboarding guide
with Claude Code, Codex, ChatGPT, Cursor, or another agent. Run
claude-certification in Codex, /claude-certification in Claude Code, or ask
another host to use claude-certification. It chooses a track, creates a
persistent route in CLAUDE-CERTIFICATION.md, teaches one step at a time, runs
the real labs, and gives artifact-based feedback. The same curriculum remains
available on the certification website.
The academy is independent study material based on public exam objectives. It is not
affiliated with Anthropic, does not reproduce live exam questions, and cannot guarantee
a passing score.
The learning skills
| Skill |
What it does |
start-learning |
One-time onboarding: why you're learning, placement quiz, personalized plan saved to LEARNING.md. |
learn |
The tutor loop. Warm-up recall, then the next lesson taught interactively, then its quiz; records progress and a review queue. |
course-guide |
Topic router. "Where do I learn attention?" or "my loss is NaN" → the exact lessons, with links. |
learn-mcp |
Focused Model Context Protocol (MCP) tutor. Creates MCP-LEARNING.md, follows the 17-lesson manifest, and records wire, security, reliability, and conformance evidence. |
learn-agent-skills |
Focused Agent Skills tutor. Creates AGENT-SKILLS-LEARNING.md, teaches lessons 22, 24, 25, 26, and 27, and records real-host evidence. |
claude-certification |
Certification tutor. Chooses CCAO-F, CCDV-F, CCAR-F, or CCAR-P; teaches each lesson; runs labs; reviews artifacts; administers diagnostics and mocks; saves progress. |
find-your-level |
Ten-question placement quiz. Maps your knowledge to a starting phase and produces a personalized path with hour estimates. |
check-understanding <phase> |
Per-phase quiz, eight questions, with feedback and specific lessons to review. Use the Codex, Claude Code, or natural-language form in the invocation table above. |
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Read the core curriculum as a book
The 20-phase core curriculum under phases/ com
一套免费、开源的 AI 工程课程，共 20 个阶段、523 节课（约 342 小时），从原始数学开始，一直教到构建自主智能体集群，使用 Python、TypeScript、Rust 和 Julia 编程。