ai-engineering-from-scratch
Learn it. Build it. Ship it for others.
Install / Use
claude mcp add rohitg00 -- npx -y github:rohitg00/ai-engineering-from-scratchIf the server publishes to npm under a different name, use that package instead — check the repo README.
MCP Server
Model Context Protocol server
Quality Score
Category
AI & Machine LearningSupported Platforms
Skill content
View source on GitHubFrom the creator of Agent Memory - #1 Persistent memory ⭐ <a href="https://github.com/rohitg00/agentmemory/stargazers"><img src="https://img.shields.io/github/stars/rohitg00/agentmemory?style=flat-square&labelColor=fafaf5&color=3553ff" alt="GitHub stars"></a> which naturally works with any agents or chat assistants.
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<!-- STATS:START (generated from site/stats.json by build.js — do not edit by hand) --> <p align="center"><sub><b>150,639</b> readers · <b>241,669</b> page views in the last 30 days · as of 2026-06-07</sub></p> <!-- STATS:END -->84% of students already use AI tools. Only 18% feel prepared to use them professionally. This curriculum closes that gap.
503 lessons. 20 phases. ~320 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 learning in 30 seconds
Your coding agent becomes your tutor. Two commands, no clone, no setup:
npx skills add rohitg00/ai-engineering-from-scratch
Then, inside your agent:
/start-learning
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, /learn teaches
one lesson per session — concept, math, code, quiz — streaming lessons straight
from this repo, and /course-guide <topic> jumps you to the exact lesson that
covers anything you are stuck on.
Works with Claude Code, Cursor, Codex, OpenClaw, Hermes, or any agent that
reads a SKILL.md directory — the installer asks which agents to set up. No
agent? Read the same 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, 503 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). Install the learning skills into any agent and let the course drive itself:
npx skills add rohitg00/ai-engineering-from-scratch
/start-learning # interview + placement quiz -> personalized plan in LEARNING.md
/learn # next lesson, taught interactively: concept -> math -> code -> quiz
/course-guide rag # "which lessons teach X?" -> exact lessons + links
Lessons stream from this repo as you go — no clone needed. Progress lives in
LEARNING.md in your project, so every session resumes where you left off.
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
python 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 /learn 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 to choose a track, create a persistent route in
CLAUDE-CERTIFICATION.md, learn one step at a time, execute the real labs, and
get 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 (any agent: Claude, Cursor, Codex, OpenClaw, Hermes, …)
| 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. |
| /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. |
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Read the core curriculum as a book
The 20-phase core curriculum under phases/ compiles into a six-volume book series. EPUB and PDF are built by CI from the same core lesson sources and attached to every GitHub release; the links below always resolve to the newest release. Volume numbers index the series, not versions: each copy carries a dated edition stamp, and older editions stay downloadable from their release.
Certification curricula are intentionally not converted into the books. Their AI tutor state, runnable labs, interactive figures, diagnostics, and timed mocks remain first-class on GitHub and the website.
| Vol | Title | Phases | Download | |-----|-------|--------|----------| | 1 | Foundations · Math, Tooling, and Classical Machine Learning | 00-02 | [EPUB](https://github.com/rohitg00/ai-engineering-from-scr
Truncated for display — read the full file on GitHub.
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