find-your-level
rohitg00/ai-engineering-from-scratch · skills.sh
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rohitg00/ai-engineering-from-scratch/find-your-level
README
<p align="center">
<img src="assets/banner.svg" alt="AI Engineering from Scratch — reference manual banner" width="100%">
</p>
<p align="center">
<b>Read in your language:</b>
<a href="i18n/es/README.md">Español</a> ·
<a href="i18n/fr/README.md">Français</a> ·
<a href="i18n/pt/README.md">Português</a> ·
<a href="i18n/de/README.md">Deutsch</a> ·
<a href="i18n/it/README.md">Italiano</a> ·
<a href="i18n/zh/README.md">简体中文</a> ·
<a href="i18n/ja/README.md">日本語</a> ·
<a href="i18n/ko/README.md">한국어</a> ·
<a href="i18n/hi/README.md">हिन्दी</a> ·
<a href="i18n/ar/README.md">العربية</a> ·
<a href="i18n/ru/README.md">Русский</a> ·
<a href="i18n/tr/README.md">Türkçe</a>
<br><sub>Translated landing pages, committed to the repo. English is canonical; lesson pages are machine-translated on the <code>translations</code> branch. See <a href="docs/i18n.md">docs/i18n.md</a>.</sub>
</p>
<p align="center">
<a href="LICENSE"><img src="https://img.shields.io/badge/license-MIT-1a1a1a?style=flat-square&labelColor=fafaf5" alt="MIT License"></a>
<a href="ROADMAP.md"><img src="https://img.shields.io/badge/lessons-523-3553ff?style=flat-square&labelColor=fafaf5" alt="523 lessons"></a>
<a href="#contents"><img src="https://img.shields.io/badge/phases-20-3553ff?style=flat-square&labelColor=fafaf5" alt="20 phases"></a>
<a href="https://github.com/rohitg00/ai-engineering-from-scratch/stargazers"><img src="https://img.shields.io/github/stars/rohitg00/ai-engineering-from-scratch?style=flat-square&labelColor=fafaf5&color=3553ff" alt="GitHub stars"></a>
<a href="https://aiengineeringfromscratch.com"><img src="https://img.shields.io/badge/web-aiengineeringfromscratch.com-3553ff?style=flat-square&labelColor=fafaf5" alt="Website"></a>
</p>
## From the creator of [Agent Memory - #1 Persistent memory ⭐](https://github.com/rohitg00/agentmemory) <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.
```text
░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒
```
> **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.
<!-- STATS:START (generated from site/stats.json by build.js — do not edit by hand) -->
<p align="center"><sub><b>114,584</b> readers · <b>181,995</b> page views in the last 30 days · as of 2026-08-29</sub></p>
<!-- STATS:END -->
## 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](phases/00-setup-and-tooling/) | [Dev Environment](https://aiengineeringfromscratch.com/lesson?path=phases/00-setup-and-tooling/01-dev-environment) |
| I know Python and want math plus ML foundations | [Phase 1: Math Foundations](phases/01-math-foundations/) | [Linear Algebra Intuition](https://aiengineeringfromscratch.com/lesson?path=phases/01-math-foundations/01-linear-algebra-intuition) |
| I want to build production LLM applications | [Phase 11: LLM Engineering](phases/11-llm-engineering/) | [Prompt Engineering](https://aiengineeringfromscratch.com/lesson?path=phases/11-llm-engineering/01-prompt-engineering) |
| I want to build agents | [Phase 14: Agent Engineering](phases/14-agent-engineering/) | [The Agent Loop](https://aiengineeringfromscratch.com/lesson?path=phases/14-agent-engineering/01-the-agent-loop) |
| I want to use coding agents on real repositories | [Agent-Assisted Engineering path](learning-paths/using-coding-agents.json) | [Agent-Assisted Engineering](https://aiengineeringfromscratch.com/lesson?path=phases/14-agent-engineering/31-agent-workbench-why-models-fail&learningPath=using-coding-agents) |
| I want to shape the right build before implementation | [Product Judgment and Delivery path](learning-paths/shaping-the-build.json) | [Product Judgment and Delivery](https://aiengineeringfromscratch.com/lesson?path=phases/14-agent-engineering/47-outcomes-before-output&learningPath=shaping-the-build) |
| I want to build with Model Context Protocol (MCP) | [Model Context Protocol (MCP) route](phases/13-tools-and-protocols/README.md#model-context-protocol-mcp-path) | [Model Context Protocol (MCP) path](https://aiengineeringfromscratch.com/lesson?path=phases/13-tools-and-protocols/06-mcp-fundamentals&learningPath=model-context-protocol) |
| I want to write and ship Agent Skills | [Focused Agent Skills route](phases/13-tools-and-protocols/README.md#agent-skills-fast-path) | [Agent Skills path](https://aiengineeringfromscratch.com/lesson?path=phases/13-tools-and-protocols/22-skills-and-agent-sdks&learningPath=agent-skills) |
| I want to prepare for a Claude certification | [Certification onboarding](certifications/claude/GETTING_STARTED.md) | [Certification Academy](https://aiengineeringfromscratch.com/certifications.html) |
Not sure where you fit? Use the [`start-learning` placement tutor](skills/start-learning/SKILL.md)
or the [website prerequisites guide](https://aiengineeringfromscratch.com/prereqs.html).
Compare four core domains and six career routes in the [AI Engineering Learning Paths](https://aiengineeringfromscratch.com/learning-paths.html).
### 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
```bash
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:
```bash
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:
```bash
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](learning-paths/model-context-protocol.json).
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](https://aiengineeringfromscratch.com/lesson?path=phases/13-tools-and-protocols/22-skills-and-agent-sdks&learningPath=agent-skills).
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](https://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.
```text
░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒
```
## 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.
```mermaid
%%{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
```
```text
░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒
```
## The shape of a lesson
Each lesson lives in its own folder, with the same structure across the entire curriculum:
```text
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.
```mermaid
%%{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:
```bash
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](https://aiengineeringfromscratch.com) or expand a phase under
[Contents](#contents). No setup, no cloning.
**Option C — clone and run.**
```bash
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](certifications/claude/README.md) 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](certifications/claude/GETTING_STARTED.md)
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](https://aiengineeringfromscratch.com/certifications.html).
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`](skills/start-learning/SKILL.md) | One-time onboarding: why you're learning, placement quiz, personalized plan saved to `LEARNING.md`. |
| [`learn`](skills/learn/SKILL.md) | The tutor loop. Warm-up recall, then the next lesson taught interactively, then its quiz; records progress and a review queue. |
| [`course-guide`](skills/course-guide/SKILL.md) | Topic router. "Where do I learn attention?" or "my loss is NaN" → the exact lessons, with links. |
| [`learn-mcp`](skills/learn-mcp/SKILL.md) | 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`](skills/learn-agent-skills/SKILL.md) | Focused Agent Skills tutor. Creates `AGENT-SKILLS-LEARNING.md`, teaches lessons 22, 24, 25, 26, and 27, and records real-host evidence. |
| [`claude-certification`](skills/claude-certification/SKILL.md) | 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`](skills/find-your-level/SKILL.md) | Ten-question placement quiz. Maps your knowledge to a starting phase and produces a personalized path with hour estimates. |
| [`check-understanding <phase>`](skills/check-understanding/SKILL.md) | 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. |
```text
░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒
```
## 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](https://github.com/rohitg00/ai-engineering-from-scratch/releases); 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-scratch/releases/latest/download/aiefs-vol1-foundations.epub) · [PDF](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol1-foundations.pdf) |
| 2 | Deep Learning · Networks, Vision, and Speech | 03, 04, 06 | [EPUB](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol2-deep-learning.epub) · [PDF](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol2-deep-learning.pdf) |
| 3 | Language · NLP Foundations and the Transformer | 05, 07 | [EPUB](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol3-language.epub) · [PDF](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol3-language.pdf) |
| 4 | Large Language Models · Generation, Reinforcement, Pretraining, and Engineering | 08-11 | [EPUB](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol4-llms.epub) · [PDF](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol4-llms.pdf) |
| 5 | Agents · Multimodality, Protocols, Autonomy, and Swarms | 12-16 | [EPUB](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol5-agents.epub) · [PDF](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol5-agents.pdf) |
| 6 | Production · Infrastructure, Safety, and Capstones | 17-19 | [EPUB](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol6-production.epub) · [PDF](https://github.com/rohitg00/ai-engineering-from-scratch/releases/latest/download/aiefs-vol6-production.pdf) |
The book is the snapshot; this repository is the living edition. Every chapter ends with links back to the lesson's animated figures, quiz, and runnable code. Build locally with `python3 scripts/build_book.py` (pandoc required); pipeline details in [book/README.md](book/README.md).
```text
░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒
```
## Every lesson ships something
Other curricula end with *"congratulations, you learned X."* Each lesson here ends with a
**reusable tool** you can install or paste into your daily workflow.
<table>
<tr>
<th align="left" width="25%"><img src="site/assets/figures/001-a-prompts.svg" width="96" height="96" alt="FIG_001.A prompts"/><br/><sub>FIG_001 · A</sub><br/><b>PROMPTS</b></th>
<th align="left" width="25%"><img src="site/assets/figures/001-b-skills.svg" width="96" height="96" alt="FIG_001.B skills"/><br/><sub>FIG_001 · B</sub><br/><b>SKILLS</b></th>
<th align="left" width="25%"><img src="site/assets/figures/001-c-agents.svg" width="96" height="96" alt="FIG_001.C agents"/><br/><sub>FIG_001 · C</sub><br/><b>AGENTS</b></th>
<th align="left" width="25%"><img src="site/assets/figures/001-d-mcp-servers.svg" width="96" height="96" alt="FIG_001.D MCP servers"/><br/><sub>FIG_001 · D</sub><br/><b>MCP SERVERS</b></th>
</tr>
<tr>
<td valign="top">Paste into any AI assistant for expert-level help on a narrow task.</td>
<td valign="top">Drop into Claude, Cursor, Codex, OpenClaw, Hermes, or any agent that reads <code>SKILL.md</code>.</td>
<td valign="top">Deploy as autonomous workers — you wrote the loop yourself in Phase 14.</td>
<td valign="top">Plug into any MCP-compatible client. Built end-to-end in Phase 13.</td>
</tr>
</table>
> Install the lot with `python3 scripts/install_skills.py <target>`. Real tools, not homework.
> By the end of the curriculum, you have a portfolio of 523 artifacts you actually
> understand because you built them.
### FIG_002 · A worked sample
Phase 14, lesson 1: the agent loop. ~120 lines of pure Python, no dependencies.
<table>
<tr>
<td valign="top" width="50%">
**`code/agent_loop.py`** <sub><i>build it</i></sub>
```python
def run(query, tools):
history = [user(query)]
for step in range(MAX_STEPS):
msg = llm(history)
if msg.tool_calls:
for call in msg.tool_calls:
result = tools[call.name](**call.args)
history.append(tool_result(call.id, result))
continue
return msg.content
raise StepLimitExceeded
```
</td>
<td valign="top" width="50%">
**`outputs/skill-agent-loop.md`** <sub><i>ship it</i></sub>
```markdown
---
name: agent-loop
description: ReAct-style loop for any tool list
phase: 14
lesson: 01
---
Implement a minimal agent loop that...
```
**`outputs/prompt-debug-agent.md`**
```markdown
You are an agent debugger. Given the trace
of an agent run, identify the step where
the agent went wrong and explain why...
```
</td>
</tr>
</table>
```text
░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒░░░▒▒▒
```
<a id="contents"></a>
## Contents
Twenty phases. Click any phase to expand its lesson list.
<a id="phase-0"></a>
### Phase 0: Setup & Tooling `12 lessons`
> Get your environment ready for everything that follows.
| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | [Dev Environment](phases/00-setup-and-tooling/01-dev-environment/) | Build | Python |
| 02 | [Git & Collaboration](phases/00-setup-and-tooling/02-git-and-collaboration/) | Learn | — |
| 03 | [GPU Setup & Cloud](phases/00-setup-and-tooling/03-gpu-setup-and-cloud/) | Build | Python |
| 04 | [APIs & Keys](phases/00-setup-and-tooling/04-apis-and-keys/) | Build | Python |
| 05 | [Jupyter Notebooks](phases/00-setup-and-tooling/05-jupyter-notebooks/) | Build | Python |
| 06 | [Python Environments](phases/00-setup-and-tooling/06-python-environments/) | Build | Shell |
| 07 | [Docker for AI](phases/00-setup-and-tooling/07-docker-for-ai/) | Build | Docker |
| 08 | [Editor Setup](phases/00-setup-and-tooling/08-editor-setup/) | Build | — |
| 09 | [Data Management](phases/00-setup-and-tooling/09-data-management/) | Build | Python |
| 10 | [Terminal & Shell](phases/00-setup-and-tooling/10-terminal-and-shell/) | Learn | — |
| 11 | [Linux for AI](phases/00-setup-and-tooling/11-linux-for-ai/) | Learn | — |
| 12 | [Debugging & Profiling](phases/00-setup-and-tooling/12-debugging-and-profiling/) | Build | Python |
<details id="phase-1">
<summary><b>Phase 1 — Math Foundations</b> <code>22 lessons</code> <em>The intuition behind every AI algorithm, through code.</em></summary>
<br/>
| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | [Linear Algebra Intuition](phases/01-math-foundations/01-linear-algebra-intuition/) | Learn | Python, Julia |
| 02 | [Vectors, Matrices & Operations](phases/01-math-foundations/02-vectors-matrices-operations/) | Build | Python, Julia |
| 03 | [Matrix Transformations & Eigenvalues](phases/01-math-foundations/03-matrix-transformations/) | Build | Python, Julia |
| 04 | [Calculus for ML: Derivatives & Gradients](phases/01-math-foundations/04-calculus-for-ml/) | Learn | Python |
| 05 | [Chain Rule & Automatic Differentiation](phases/01-math-foundations/05-chain-rule-and-autodiff/) | Build | Python |
| 06 | [Probability & Distributions](phases/01-math-foundations/06-probability-and-distributions/) | Learn | Python |
| 07 | [Bayes' Theorem & Statistical Thinking](phases/01-math-foundations/07-bayes-theorem/) | Build | Python |
| 08 | [Optimization: Gradient Descent Family](phases/01-math-foundations/08-optimization/) | Build | Python |
| 09 | [Information Theory: Entropy, KL Divergence](phases/01-math-foundations/09-information-theory/) | Learn | Python |
| 10 | [Dimensionality Reduction: PCA, t-SNE, UMAP](phases/01-math-foundations/10-dimensionality-reduction/) | Build | Python |
| 11 | [Singular Value Decomposition](phases/01-math-foundations/11-singular-value-decomposition/) | Build | Python, Julia |
| 12 | [Tensor Operations](phases/01-math-foundations/12-tensor-operations/) | Build | Python |
| 13 | [Numerical Stability](phases/01-math-foundations/13-numerical-stability/) | Build | Python |
| 14 | [Norms & Distances](phases/01-math-foundations/14-norms-and-distances/) | Build | Python |
| 15 | [Statistics for ML](phases/01-math-foundations/15-statistics-for-ml/) | Build | Python |
| 16 | [Sampling Methods](phases/01-math-foundations/16-sampling-methods/) | Build | Python |
| 17 | [Linear Systems](phases/01-math-foundations/17-linear-systems/) | Build | Python |
| 18 | [Convex Optimization](phases/01-math-foundations/18-convex-optimization/) | Build | Python |
| 19 | [Complex Numbers for AI](phases/01-math-foundations/19-complex-numbers/) | Learn | Python |
| 20 | [The Fourier Transform](phases/01-math-foundations/20-fourier-transform/) | Build | Python |
| 21 | [Graph Theory for ML](phases/01-math-foundations/21-graph-theory/) | Build | Python |
| 22 | [Stochastic Processes](phases/01-math-foundations/22-stochastic-processes/) | Learn | Python |
</details>
<details id="phase-2">
<summary><b>Phase 2 — ML Fundamentals</b> <code>18 lessons</code> <em>Classical ML — still the backbone of most production AI.</em></summary>
<br/>
| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | [What Is Machine Learning](phases/02-ml-fundamentals/01-what-is-machine-learning/) | Learn | Python |
| 02 | [Linear Regression from Scratch](phases/02-ml-fundamentals/02-linear-regression/) | Build | Python |
| 03 | [Logistic Regression & Classification](phases/02-ml-fundamentals/03-logistic-regression/) | Build | Python |
| 04 | [Decision Trees & Random Forests](phases/02-ml-fundamentals/04-decision-trees/) | Build | Python |
| 05 | [Support Vector Machines](phases/02-ml-fundamentals/05-support-vector-machines/) | Build | Python |
| 06 | [KNN & Distance Metrics](phases/02-ml-fundamentals/06-knn-and-distances/) | Build | Python |
| 07 | [Unsupervised Learning: K-Means, DBSCAN](phases/02-ml-fundamentals/07-unsupervised-learning/) | Build | Python |
| 08 | [Feature Engineering & Selection](phases/02-ml-fundamentals/08-feature-engineering/) | Build | Python |
| 09 | [Model Evaluation: Metrics, Cross-Validation](phases/02-ml-fundamentals/09-model-evaluation/) | Build | Python |
| 10 | [Bias, Variance & the Learning Curve](phases/02-ml-fundamentals/10-bias-variance/) | Learn | Python |
| 11 | [Ensemble Methods: Boosting, Bagging, Stacking](phases/02-ml-fundamentals/11-ensemble-methods/) | Build | Python |
| 12 | [Hyperparameter Tuning](phases/02-ml-fundamentals/12-hyperparameter-tuning/) | Build | Python |
| 13 | [ML Pipelines & Experiment Tracking](phases/02-ml-fundamentals/13-ml-pipelines/) | Build | Python |
| 14 | [Naive Bayes](phases/02-ml-fundamentals/14-naive-bayes/) | Build | Python |
| 15 | [Time Series Fundamentals](phases/02-ml-fundamentals/15-time-series/) | Build | Python |
| 16 | [Anomaly Detection](phases/02-ml-fundamentals/16-anomaly-detection/) | Build | Python |
| 17 | [Handling Imbalanced Data](phases/02-ml-fundamentals/17-imbalanced-data/) | Build | Python |
| 18 | [Feature Selection](phases/02-ml-fundamentals/18-feature-selection/) | Build | Python |
</details>
<details id="phase-3">
<summary><b>Phase 3 — Deep Learning Core</b> <code>13 lessons</code> <em>Neural networks from first principles. No frameworks until you build one.</em></summary>
<br/>
| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | [The Perceptron: Where It All Started](phases/03-deep-learning-core/01-the-perceptron/) | Build | Python |
| 02 | [Multi-Layer Networks & Forward Pass](phases/03-deep-learning-core/02-multi-layer-networks/) | Build | Python |
| 03 | [Backpropagation from Scratch](phases/03-deep-learning-core/03-backpropagation/) | Build | Python |
| 04 | [Activation Functions: ReLU, Sigmoid, GELU & Why](phases/03-deep-learning-core/04-activation-functions/) | Build | Python |
| 05 | [Loss Functions: MSE, Cross-Entropy, Contrastive](phases/03-deep-learning-core/05-loss-functions/) | Build | Python |
| 06 | [Optimizers: SGD, Momentum, Adam, AdamW](phases/03-deep-learning-core/06-optimizers/) | Build | Python |
| 07 | [Regularization: Dropout, Weight Decay, BatchNorm](phases/03-deep-learning-core/07-regularization/) | Build | Python |
| 08 | [Weight Initialization & Training Stability](phases/03-deep-learning-core/08-weight-initialization/) | Build | Python |
| 09 | [Learning Rate Schedules & Warmup](phases/03-deep-learning-core/09-learning-rate-schedules/) | Build | Python |
| 10 | [Build Your Own Mini Framework](phases/03-deep-learning-core/10-mini-framework/) | Build | Python |
| 11 | [Introduction to PyTorch](phases/03-deep-learning-core/11-intro-to-pytorch/) | Build | Python |
| 12 | [Introduction to JAX](phases/03-deep-learning-core/12-intro-to-jax/) | Build | Python |
| 13 | [Debugging Neural Networks](phases/03-deep-learning-core/13-debugging-neural-networks/) | Build | Python |
</details>
<details id="phase-4">
<summary><b>Phase 4 — Computer Vision</b> <code>28 lessons</code> <em>From pixels to understanding — image, video, 3D, VLMs, and world models.</em></summary>
<br/>
| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | [Image Fundamentals: Pixels, Channels, Color Spaces](phases/04-computer-vision/01-image-fundamentals/) | Learn | Python |
| 02 | [Convolutions from Scratch](phases/04-computer-vision/02-convolutions-from-scratch/) | Build | Python |
| 03 | [CNNs: LeNet to ResNet](phases/04-computer-vision/03-cnns-lenet-to-resnet/) | Build | Python |
| 04 | [Image Classification](phases/04-computer-vision/04-image-classification/) | Build | Python |
| 05 | [Transfer Learning & Fine-Tuning](phases/04-computer-vision/05-transfer-learning/) | Build | Python |
| 06 | [Object Detection — YOLO from Scratch](phases/04-computer-vision/06-object-detection-yolo/) | Build | Python |
| 07 | [Semantic Segmentation — U-Net](phases/04-computer-vision/07-semantic-segmentation-unet/) | Build | Python |
| 08 | [Instance Segmentation — Mask R-CNN](phases/04-computer-vision/08-instance-segmentation-mask-rcnn/) | Build | Python |
| 09 | [Image Generation — GANs](phases/04-computer-vision/09-image-generation-gans/) | Build | Python |
| 10 | [Image Generation — Diffusion Models](phases/04-computer-vision/10-image-generation-diffusion/) | Build | Python |
| 11 | [Stable Diffusion — Architecture & Fine-Tuning](phases/04-computer-vision/11-stable-diffusion/) | Build | Python |
| 12 | [Video Understanding — Temporal Modeling](phases/04-computer-vision/12-video-understanding/) | Build | Python |
| 13 | [3D Vision: Point Clouds, NeRFs](phases/04-computer-vision/13-3d-vision-nerf/) | Build | Python |
| 14 | [Vision Transformers (ViT)](phases/04-computer-vision/14-vision-transformers/) | Build | Python |
| 15 | [Real-Time Vision: Edge Deployment](phases/04-computer-vision/15-real-time-edge/) | Build | Python |
| 16 | [Build a Complete Vision Pipeline](phases/04-computer-vision/16-vision-pipeline-capstone/) | Build | Python |
| 17 | [Self-Supervised Vision — SimCLR, DINO, MAE](phases/04-computer-vision/17-self-supervised-vision/) | Build | Python |
| 18 | [Open-Vocabulary Vision — CLIP](phases/04-computer-vision/18-open-vocab-clip/) | Build | Python |
| 19 | [OCR & Document Understanding](phases/04-computer-vision/19-ocr-document-understanding/) | Build | Python |
| 20 | [Image Retrieval & Metric Learning](phases/04-computer-vision/20-image-retrieval-metric/) | Build | Python |
| 21 | [Keypoint Detection & Pose Estimation](phases/04-computer-vision/21-keypoint-pose/) | Build | Python |
| 22 | [3D Gaussian Splatting from Scratch](phases/04-computer-vision/22-3d-gaussian-splatting/) | Build | Python |
| 23 | [Diffusion Transformers & Rectified Flow](phases/04-computer-vision/23-diffusion-transformers-rectified-flow/) | Build | Python |
| 24 | [SAM 3 & Open-Vocabulary Segmentation](phases/04-computer-vision/24-sam3-open-vocab-segmentation/) | Build | Python |
| 25 | [Vision-Language Models (ViT-MLP-LLM)](phases/04-computer-vision/25-vision-language-models/) | Build | Python |
| 26 | [Monocular Depth & Geometry Estimation](phases/04-computer-vision/26-monocular-depth/) | Build | Python |
| 27 | [Multi-Object Tracking & Video Memory](phases/04-computer-vision/27-multi-object-tracking/) | Build | Python |
| 28 | [World Models & Video Diffusion](phases/04-computer-vision/28-world-models-video-diffusion/) | Build | Python |
</details>
<details id="phase-5">
<summary><b>Phase 5 — NLP: Foundations to Advanced</b> <code>29 lessons</code> <em>Language is the interface to intelligence.</em></summary>
<br/>
| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | [Text Processing: Tokenization, Stemming, Lemmatization](phases/05-nlp-foundations-to-advanced/01-text-processing/) | Build | Python |
| 02 | [Bag of Words, TF-IDF & Text Representation](phases/05-nlp-foundations-to-advanced/02-bag-of-words-tfidf/) | Build | Python |
| 03 | [Word Embeddings: Word2Vec from Scratch](phases/05-nlp-foundations-to-advanced/03-word-embeddings-word2vec/) | Build | Python |
| 04 | [GloVe, FastText & Subword Embeddings](phases/05-nlp-foundations-to-advanced/04-glove-fasttext-subword/) | Build | Python |
| 05 | [Sentiment Analysis](phases/05-nlp-foundations-to-advanced/05-sentiment-analysis/) | Build | Python |
| 06 | [Named Entity Recognition (NER)](phases/05-nlp-foundations-to-advanced/06-named-entity-recognition/) | Build | Python |
| 07 | [POS Tagging & Syntactic Parsing](phases/05-nlp-foundations-to-advanced/07-pos-tagging-parsing/) | Build | Python |
| 08 | [Text Classification — CNNs & RNNs for Text](phases/05-nlp-foundations-to-advanced/08-cnns-rnns-for-text/) | Build | Python |
| 09 | [Sequence-to-Sequence Models](phases/05-nlp-foundations-to-advanced/09-sequence-to-sequence/) | Build | Python |
| 10 | [Attention Mechanism — The Breakthrough](phases/05-nlp-foundations-to-advanced/10-attention-mechanism/) | Build | Python |
| 11 | [Machine Translation](phases/05-nlp-foundations-to-advanced/11-machine-translation/) | Build | Python |
| 12 | [Text Summarization](phases/05-nlp-foundations-to-advanced/12-text-summarization/) | Build | Python |
| 13 | [Question Answering Systems](phases/05-nlp-foundations-to-advanced/13-question-answering/) | Build | Python |
| 14 | [Information Retrieval & Search](phases/05-nlp-foundations-to-advanced/14-information-retrieval-search/) | Build | Python |
| 15 | [Topic Modeling: LDA, BERTopic](phases/05-nlp-foundations-to-advanced/15-topic-modeling/) | Build | Python |
| 16 | [Text Generation](phases/05-nlp-foundations-to-advanced/16-text-generation-pre-transformer/) | Build | Python |
| 17 | [Chatbots: Rule-Based to Neural](phases/05-nlp-foundations-to-advanced/17-chatbots-rule-to-neural/) | Build | Python |
| 18 | [Multilingual NLP](phases/05-nlp-foundations-to-advanced/18-multilingual-nlp/) | Build | Python |
| 19 | [Subword Tokenization: BPE, WordPiece, Unigram, SentencePiece](phases/05-nlp-foundations-to-advanced/19-subword-tokenization/) | Learn | Python |
| 20 | [Structured Outputs & Constrained Decoding](phases/05-nlp-foundations-to-advanced/20-structured-outputs-constrained-decoding/) | Build | Python |
| 21 | [NLI & Textual Entailment](phases/05-nlp-foundations-to-advanced/21-nli-textual-entailment/) | Learn | Python |
| 22 | [Embedding Models Deep Dive](phases/05-nlp-foundations-to-advanced/22-embedding-models-deep-dive/) | Learn | Python |
| 23 | [Chunking Strategies for RAG](phases/05-nlp-foundations-to-advanced/23-chunking-strategies-rag/) | Build | Python |
| 24 | [Coreference Resolution](phases/05-nlp-foundations-to-advanced/24-coreference-resolution/) | Learn | Python |
| 25 | [Entity Linking & Disambiguation](phases/05-nlp-foundations-to-advanced/25-entity-linking/) | Build | Python |
| 26 | [Relation Extraction & Knowledge Graph Construction](phases/05-nlp-foundations-to-advanced/26-relation-extraction-kg/) | Build | Python |
| 27 | [LLM Evaluation: RAGAS, DeepEval, G-Eval](phases/05-nlp-foundations-to-advanced/27-llm-evaluation-frameworks/) | Build | Python |
| 28 | [Long-Context Evaluation: NIAH, RULER, LongBench, MRCR](phases/05-nlp-foundations-to-advanced/28-long-context-evaluation/) | Learn | Python |
| 29 | [Dialogue State Tracking](phases/05-nlp-foundations-to-advanced/29-dialogue-state-tracking/) | Build | Python |
</details>
<details id="phase-6">
<summary><b>Phase 6 — Speech & Audio</b> <code>17 lessons</code> <em>Hear, understand, speak.</em></summary>
<br/>
| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | [Audio Fundamentals: Waveforms, Sampling, FFT](phases/06-speech-and-audio/01-audio-fundamentals) | Learn | Python |
| 02 | [Spectrograms, Mel Scale & Audio Features](phases/06-speech-and-audio/02-spectrograms-mel-features) | Build | Python |
| 03 | [Audio Classification](phases/06-speech-and-audio/03-audio-classification) | Build | Python |
| 04 | [Speech Recognition (ASR)](phases/06-speech-and-audio/04-speech-recognition-asr) | Build | Python |
| 05 | [Whisper: Architecture & Fine-Tuning](phases/06-speech-and-audio/05-whisper-architecture-finetuning) | Build | Python |
| 06 | [Speaker Recognition & Verification](phases/06-speech-and-audio/06-speaker-recognition-verification) | Build | Python |
| 07 | [Text-to-Speech (TTS)](phases/06-speech-and-audio/07-text-to-speech) | Build | Python |
| 08 | [Voice Cloning & Voice Conversion](phases/06-speech-and-audio/08-voice-cloning-conversion) | Build | Python |
| 09 | [Music Generation](phases/06-speech-and-audio/09-music-generation) | Build | Python |
| 10 | [Audio-Language Models](phases/06-speech-and-audio/10-audio-language-models) | Build | Python |
| 11 | [Real-Time Audio Processing](phases/06-speech-and-audio/11-real-time-audio-processing) | Build | Python |
| 12 | [Build a Voice Assistant Pipeline](phases/06-speech-and-audio/12-voice-assistant-pipeline) | Build | Python |
| 13 | [Neural Audio Codecs — EnCodec, SNAC, Mimi, DAC](phases/06-speech-and-audio/13-neural-audio-codecs) | Learn | Python |
| 14 | [Voice Activity Detection & Turn-Taking](phases/06-speech-and-audio/14-voice-activity-detection-turn-taking) | Build | Python |
| 15 | [Streaming Speech-to-Speech — Moshi, Hibiki](phases/06-speech-and-audio/15-streaming-speech-to-speech-moshi-hibiki) | Learn | Python |
| 16 | [Voice Anti-Spoofing & Audio Watermarking](phases/06-speech-and-audio/16-anti-spoofing-audio-watermarking) | Build | Python |
| 17 | [Audio Evaluation — WER, MOS, MMAU, Leaderboards](phases/06-speech-and-audio/17-audio-evaluation-metrics) | Learn | Python |
</details>
<details id="phase-7">
<summary><b>Phase 7 — Transformers Deep Dive</b> <code>16 lessons</code> <em>The architecture that changed everything.</em></summary>
<br/>
| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | [Why Transformers: The Problems with RNNs](phases/07-transformers-deep-dive/01-why-transformers/) | Learn | Python |
| 02 | [Self-Attention from Scratch](phases/07-transformers-deep-dive/02-self-attention-from-scratch/) | Build | Python |
| 03 | [Multi-Head Attention](phases/07-transformers-deep-dive/03-multi-head-attention/) | Build | Python |
| 04 | [Positional Encoding: Sinusoidal, RoPE, ALiBi](phases/07-transformers-deep-dive/04-positional-encoding/) | Build | Python |
| 05 | [The Full Transformer: Encoder + Decoder](phases/07-transformers-deep-dive/05-full-transformer/) | Build | Python |
| 06 | [BERT — Masked Language Modeling](phases/07-transformers-deep-dive/06-bert-masked-language-modeling/) | Build | Python |
| 07 | [GPT — Causal Language Modeling](phases/07-transformers-deep-dive/07-gpt-causal-language-modeling/) | Build | Python |
| 08 | [T5, BART — Encoder-Decoder Models](phases/07-transformers-deep-dive/08-t5-bart-encoder-decoder/) | Learn | Python |
| 09 | [Vision Transformers (ViT)](phases/07-transformers-deep-dive/09-vision-transformers/) | Build | Python |
| 10 | [Audio Transformers — Whisper Architecture](phases/07-transformers-deep-dive/10-audio-transformers-whisper/) | Learn | Python |
| 11 | [Mixture of Experts (MoE)](phases/07-transformers-deep-dive/11-mixture-of-experts/) | Build | Python |
| 12 | [KV Cache, Flash Attention & Inference Optimization](phases/07-transformers-deep-dive/12-kv-cache-flash-attention/) | Build | Python |
| 13 | [Scaling Laws](phases/07-transformers-deep-dive/13-scaling-laws/) | Learn | Python |
| 14 | [Build a Transformer from Scratch](phases/07-transformers-deep-dive/14-build-a-transformer-capstone/) | Build | Python |
| 15 | [Attention Variants — Sliding Window, Sparse, Differential](phases/07-transformers-deep-dive/15-attention-variants/) | Build | Python |
| 16 | [Speculative Decoding — Draft, Verify, Repeat](phases/07-transformers-deep-dive/16-speculative-decoding/) | Build | Python |
</details>
<details id="phase-8">
<summary><b>Phase 8 — Generative AI</b> <code>15 lessons</code> <em>Create images, video, audio, 3D, and more.</em></summary>
<br/>
| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | [Generative Models: Taxonomy & History](phases/08-generative-ai/01-generative-models-taxonomy-history/) | Learn | Python |
| 02 | [Autoencoders & VAE](phases/08-generative-ai/02-autoencoders-vae/) | Build | Python |
| 03 | [GANs: Generator vs Discriminator](phases/08-generative-ai/03-gans-generator-discriminator/) | Build | Python |
| 04 | [Conditional GANs & Pix2Pix](phases/08-generative-ai/04-conditional-gans-pix2pix/) | Build | Python |
| 05 | [StyleGAN](phases/08-generative-ai/05-stylegan/) | Build | Python |
| 06 | [Diffusion Models — DDPM from Scratch](phases/08-generative-ai/06-diffusion-ddpm-from-scratch/) | Build | Python |
| 07 | [Latent Diffusion & Stable Diffusion](phases/08-generative-ai/07-latent-diffusion-stable-diffusion/) | Build | Python |
| 08 | [ControlNet, LoRA & Conditioning](phases/08-generative-ai/08-controlnet-lora-conditioning/) | Build | Python |
| 09 | [Inpainting, Outpainting & Editing](phases/08-generative-ai/09-inpainting-outpainting-editing/) | Build | Python |
| 10 | [Video Generation](phases/08-generative-ai/10-video-generation/) | Build | Python |
| 11 | [Audio Generation](phases/08-generative-ai/11-audio-generation/) | Build | Python |
| 12 | [3D Generation](phases/08-generative-ai/12-3d-generation/) | Build | Python |
| 13 | [Flow Matching & Rectified Flows](phases/08-generative-ai/13-flow-matching-rectified-flows/) | Build | Python |
| 14 | [Evaluation: FID, CLIP Score](phases/08-generative-ai/14-evaluation-fid-clip-score/) | Build | Python |
| 19 | [Visual Autoregressive Modeling (VAR): Next-Scale Prediction](phases/08-generative-ai/19-visual-autoregressive-var/) | Build | Python |
</details>
<details id="phase-9">
<summary><b>Phase 9 — Reinforcement Learning</b> <code>12 lessons</code> <em>The foundation of RLHF and game-playing AI.</em></summary>
<br/>
| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | [MDPs, States, Actions & Rewards](phases/09-reinforcement-learning/01-mdps-states-actions-rewards/) | Learn | Python |
| 02 | [Dynamic Programming](phases/09-reinforcement-learning/02-dynamic-programming/) | Build | Python |
| 03 | [Monte Carlo Methods](phases/09-reinforcement-learning/03-monte-carlo-methods/) | Build | Python |
| 04 | [Q-Learning, SARSA](phases/09-reinforcement-learning/04-q-learning-sarsa/) | Build | Python |
| 05 | [Deep Q-Networks (DQN)](phases/09-reinforcement-learning/05-dqn/) | Build | Python |
| 06 | [Policy Gradients — REINFORCE](phases/09-reinforcement-learning/06-policy-gradients-reinforce/) | Build | Python |
| 07 | [Actor-Critic — A2C, A3C](phases/09-reinforcement-learning/07-actor-critic-a2c-a3c/) | Build | Python |
| 08 | [PPO](phases/09-reinforcement-learning/08-ppo/) | Build | Python |
| 09 | [Reward Modeling & RLHF](phases/09-reinforcement-learning/09-reward-modeling-rlhf/) | Build | Python |
| 10 | [Multi-Agent RL](phases/09-reinforcement-learning/10-multi-agent-rl/) | Build | Python |
| 11 | [Sim-to-Real Transfer](phases/09-reinforcement-learning/11-sim-to-real-transfer/) | Build | Python |
| 12 | [RL for Games](phases/09-reinforcement-learning/12-rl-for-games/) | Build | Python |
</details>
<details id="phase-10">
<summary><b>Phase 10 — LLMs from Scratch</b> <code>24 lessons</code> <em>Build, train, and understand large language models.</em></summary>
<br/>
| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | [Tokenizers: BPE, WordPiece, SentencePiece](phases/10-llms-from-scratch/01-tokenizers/) | Build | Python, Rust |
| 02 | [Building a Tokenizer from Scratch](phases/10-llms-from-scratch/02-building-a-tokenizer/) | Build | Python |
| 03 | [Data Pipelines for Pre-Training](phases/10-llms-from-scratch/03-data-pipelines/) | Build | Python |
| 04 | [Pre-Training a Mini GPT (124M)](phases/10-llms-from-scratch/04-pre-training-mini-gpt/) | Build | Python |
| 05 | [Distributed Training, FSDP, DeepSpeed](phases/10-llms-from-scratch/05-scaling-distributed/) | Build | Python |
| 06 | [Instruction Tuning — SFT](phases/10-llms-from-scratch/06-instruction-tuning-sft/) | Build | Python |
| 07 | [RLHF — Reward Model + PPO](phases/10-llms-from-scratch/07-rlhf/) | Build | Python |
| 08 | [DPO — Direct Preference Optimization](phases/10-llms-from-scratch/08-dpo/) | Build | Python |
| 09 | [Constitutional AI & Self-Improvement](phases/10-llms-from-scratch/09-constitutional-ai-self-improvement/) | Build | Python |
| 10 | [Evaluation — Benchmarks, Evals](phases/10-llms-from-scratch/10-evaluation/) | Build | Python |
| 11 | [Quantization: INT8, GPTQ, AWQ, GGUF](phases/10-llms-from-scratch/11-quantization/) | Build | Python |
| 12 | [Inference Optimization](phases/10-llms-from-scratch/12-inference-optimization/) | Build | Python |
| 13 | [Building a Complete LLM Pipeline](phases/10-llms-from-scratch/13-building-complete-llm-pipeline/) | Build | Python |
| 14 | [Open Models: Architecture Walkthroughs](phases/10-llms-from-scratch/14-open-models-architecture-walkthroughs/) | Learn | Python |
| 15 | [Speculative Decoding and EAGLE-3](phases/10-llms-from-scratch/15-speculative-decoding-eagle3/) | Build | Python |
| 16 | [Differential Attention (V2)](phases/10-llms-from-scratch/16-differential-attention-v2/) | Build | Python |
| 17 | [Native Sparse Attention (DeepSeek NSA)](phases/10-llms-from-scratch/17-native-sparse-attention/) | Build | Python |
| 18 | [Multi-Token Prediction (MTP)](phases/10-llms-from-scratch/18-multi-token-prediction/) | Build | Python |
| 19 | [DualPipe Parallelism](phases/10-llms-from-scratch/19-dualpipe-parallelism/) | Learn | Python |
| 20 | [DeepSeek-V3 Architecture Walkthrough](phases/10-llms-from-scratch/20-deepseek-v3-walkthrough/) | Learn | Python |
| 21 | [Jamba — Hybrid SSM-Transformer](phases/10-llms-from-scratch/21-jamba-hybrid-ssm-transformer/) | Learn | Python |
| 22 | [Async and Hogwild! Inference](phases/10-llms-from-scratch/22-async-hogwild-inference/) | Build | Python |
| 25 | [Speculative Decoding and EAGLE](phases/10-llms-from-scratch/25-speculative-decoding/) | Build | Python |
| 34 | [Gradient Checkpointing and Activation Recomputation](phases/10-llms-from-scratch/34-gradient-checkpointing/) | Build | Python |
</details>
<details id="phase-11">
<summary><b>Phase 11 — LLM Engineering</b> <code>17 lessons</code> <em>Put LLMs to work in production.</em></summary>
<br/>
| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | [Prompt Engineering: Techniques & Patterns](phases/11-llm-engineering/01-prompt-engineering/) | Build | Python |
| 02 | [Few-Shot, CoT, Tree-of-Thought](phases/11-llm-engineering/02-few-shot-cot/) | Build | Python |
| 03 | [Structured Outputs](phases/11-llm-engineering/03-structured-outputs/) | Build | Python |
| 04 | [Embeddings & Vector Representations](phases/11-llm-engineering/04-embeddings/) | Build | Python |
| 05 | [Context Engineering](phases/11-llm-engineering/05-context-engineering/) | Build | Python |
| 06 | [RAG: Retrieval-Augmented Generation](phases/11-llm-engineering/06-rag/) | Build | Python |
| 07 | [Advanced RAG: Chunking, Reranking](phases/11-llm-engineering/07-advanced-rag/) | Build | Python |
| 08 | [Fine-Tuning with LoRA & QLoRA](phases/11-llm-engineering/08-fine-tuning-lora/) | Build | Python |
| 09 | [Function Calling & Tool Use](phases/11-llm-engineering/09-function-calling/) | Build | Python |
| 10 | [Evaluation & Testing](phases/11-llm-engineering/10-evaluation/) | Build | Python |
| 11 | [Caching, Rate Limiting & Cost](phases/11-llm-engineering/11-caching-cost/) | Build | Python |
| 12 | [Guardrails & Safety](phases/11-llm-engineering/12-guardrails/) | Build | Python |
| 13 | [Building a Production LLM App](phases/11-llm-engineering/13-production-app/) | Build | Python |
| 14 | [Model Context Protocol (MCP)](phases/11-llm-engineering/14-model-context-protocol/) | Build | Python |
| 15 | [Prompt Caching & Context Caching](phases/11-llm-engineering/15-prompt-caching/) | Build | Python |
| 16 | [Agent State Machines — Graphs, Nodes, Checkpoints](phases/11-llm-engineering/16-langgraph-state-machines/) | Build | Python |
| 17 | [Agent Framework Tradeoffs](phases/11-llm-engineering/17-agent-framework-tradeoffs/) | Learn | Python |
</details>
<details id="phase-12">
<summary><b>Phase 12 — Multimodal AI</b> <code>25 lessons</code> <em>See, hear, read, and reason across modalities — from ViT patches to computer-use agents.</em></summary>
<br/>
| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | [Vision Transformers and the Patch-Token Primitive](phases/12-multimodal-ai/01-vision-transformer-patch-tokens/) | Learn | Python |
| 02 | [CLIP and Contrastive Vision-Language Pretraining](phases/12-multimodal-ai/02-clip-contrastive-pretraining/) | Build | Python |
| 03 | [BLIP-2 Q-Former as Modality Bridge](phases/12-multimodal-ai/03-blip2-qformer-bridge/) | Build | Python |
| 04 | [Flamingo and Gated Cross-Attention](phases/12-multimodal-ai/04-flamingo-gated-cross-attention/) | Learn | Python |
| 05 | [LLaVA and Visual Instruction Tuning](phases/12-multimodal-ai/05-llava-visual-instruction-tuning/) | Build | Python |
| 06 | [Any-Resolution Vision — Patch-n'-Pack and NaFlex](phases/12-multimodal-ai/06-any-resolution-patch-n-pack/) | Build | Python |
| 07 | [Open-Weight VLM Recipes: What Actually Matters](phases/12-multimodal-ai/07-open-weight-vlm-recipes/) | Learn | Python |
| 08 | [LLaVA-OneVision: Single, Multi, Video](phases/12-multimodal-ai/08-llava-onevision-single-multi-video/) | Build | Python |
| 09 | [Qwen-VL Family and Dynamic-FPS Video](phases/12-multimodal-ai/09-qwen-vl-family-dynamic-fps/) | Learn | Python |
| 10 | [InternVL3 Native Multimodal Pretraining](phases/12-multimodal-ai/10-internvl3-native-multimodal/) | Learn | Python |
| 11 | [Chameleon Early-Fusion Token-Only](phases/12-multimodal-ai/11-chameleon-early-fusion-tokens/) | Build | Python |
| 12 | [Emu3 Next-Token Prediction for Generation](phases/12-multimodal-ai/12-emu3-next-token-for-generation/) | Learn | Python |
| 13 | [Transfusion Autoregressive + Diffusion](phases/12-multimodal-ai/13-transfusion-autoregressive-diffusion/) | Build | Python |
| 14 | [Show-o Discrete-Diffusion Unified](phases/12-multimodal-ai/14-show-o-discrete-diffusion-unified/) | Learn | Python |
| 15 | [Janus-Pro Decoupled Encoders](phases/12-multimodal-ai/15-janus-pro-decoupled-encoders/) | Build | Python |
| 16 | [MIO Any-to-Any Streaming](phases/12-multimodal-ai/16-mio-any-to-any-streaming/) | Learn | Python |
| 17 | [Video-Language Temporal Grounding](phases/12-multimodal-ai/17-video-language-temporal-grounding/) | Build | Python |
| 18 | [Long-Video at Million-Token Context](phases/12-multimodal-ai/18-long-video-million-token/) | Build | Python |
| 19 | [Audio-Language Models: Whisper to AF3](phases/12-multimodal-ai/19-audio-language-whisper-to-af3/) | Build | Python |
| 20 | [Omni Models: Thinker-Talker Streaming](phases/12-multimodal-ai/20-omni-models-thinker-talker/) | Build | Python |
| 21 | [Embodied VLAs: RT-2, OpenVLA, π0, GR00T](phases/12-multimodal-ai/21-embodied-vlas-openvla-pi0-groot/) | Learn | Python |
| 22 | [Document and Diagram Understanding](phases/12-multimodal-ai/22-document-diagram-understanding/) | Build | Python |
| 23 | [ColPali Vision-Native Document RAG](phases/12-multimodal-ai/23-colpali-vision-native-rag/) | Build | Python |
| 24 | [Multimodal RAG and Cross-Modal Retrieval](phases/12-multimodal-ai/24-multimodal-rag-cross-modal/) | Build | Python |
| 25 | [Multimodal Agents and Computer-Use (Capstone)](phases/12-multimodal-ai/25-multimodal-agents-computer-use/) | Build | Python |
</details>
<details id="phase-13">
<summary><b>Phase 13 — Tools & Protocols</b> <code>31 lessons</code> <em>The interfaces between AI and the real world.</em></summary>
<br/>
| # | Lesson | Type | Lang |
|:---:|--------|:----:|------|
| 01 | [The Tool Interface](phases/13-tools-and-protocols/01-the-tool-interface/) | Learn | Python |
| 02 | [Function Calling Deep Dive](phases/13-tools-and-protocols/02-function-calling-deep-dive/) | Build | Python |
| 03 | [Parallel and Streaming Tool Calls](phases/13-tools-and-protocols/03-parallel-and-streaming-tool-calls/) | Build | Python |
| 04 | [Structured Output](phases/13-tools-and-protocols/04-structured-output/) | Build | Python |
| 05 | [Tool Schema Design](phases/13-tools-and-protocols/05-tool-schema-design/) | Learn | Python |
| 06 | [MCP Fundamentals: Stateless Requests and JSON-RPC](phases/13-tools-and-protocols/06-mcp-fundamentals/) | Learn | Python |
| 07 | [Building an MCP Server: Stateless Python and TypeScript](phases/13-tools-and-protocols/07-building-an-mcp-server/) | Build | Python, TypeScript |
| 08 | [Building an MCP Client: Discovery, Routing, and Dual-Era Fallback](phases/13-tools-and-protocols/08-building-an-mcp-client/) | Build | Python |
| 09 | [MCP Transports: stdio and Stateless Streamable HTTP](phases/13-tools-and-protocols/09-mcp-transports/) | Learn | Python |
| 10 | [MCP Resources and Prompts: Addressable Context for Stateless Servers](phases/13-tools-and-protocols/10-mcp-resources-and-prompts/) | Build | Python |
| 11 | [MCP Model Input: Sampling Migration and Stateless MRTR](phases/13-tools-and-protocols/11-mcp-sampling/) | Build | Python |
| 12 | [Explicit Scope and Stateless Elicitation](phases/13-tools-and-protocols/12-mcp-roots-and-elicitation/) | Build | Python |
| 13 | [MCP Tasks Extension: Durable Work on a Stateless Core](phases/13-tools-and-protocols/13-mcp-async-tasks/) | Build | Python |
| 14 | [MCP Apps on the Stateless Protocol](phases/13-tools-and-protocols/14-mcp-apps/) | Build | Python |
| 15 | [MCP Security: Poisoned Metadata, Routing, and MRTR State](phases/13-tools-and-protocols/15-mcp-security-tool-poisoning/) | Learn | Python |
| 16 | [MCP Authorization: CIMD, Issuer Binding, PKCE, and Step-Up](phases/13-tools-and-protocols/16-mcp-security-oauth-2-1/) | Build | Python |
| 17 | [Stateless MCP Gateways and Registry Admission](phases/13-tools-and-protocols/17-mcp-gateways-and-registries/) | Learn | Python |
| 18 | [MCP Auth in Production: Issuer-Bound Enrollment and Tokens](phases/13-tools-and-protocols/18-mcp-auth-production/) | Build | Python |
| 19 | [A2A Protocol](phases/13-tools-and-protocols/19-a2a-protocol/) | Build | Python |
| 20 | [OpenTelemetry GenAI](phases/13-tools-and-protocols/20-opentelemetry-genai/) | Build | Python |
| 21 | [LLM Routing Layer](phases/13-tools-and-protocols/21-llm-routing-layer/) | Learn | Python |
| 22 | [Agent Skills: Portable Contract and Runtime Boundary](phases/13-tools-and-protocols/22-skills-and-agent-sdks/) | Build | Python |
| 23 | [Capstone: Stateless Tool Ecosystem](phases/13-tools-and-protocols/23-capstone-tool-ecosystem/) | Build | Python |
| 24 | [Skill Discovery and Progressive Disclosure](phases/13-tools-and-protocols/24-skill-discovery-and-progressive-disclosure/) | Build | Python |
| 25 | [Skill Invocation and Routing](phases/13-tools-and-protocols/25-skill-invocation-and-routing/) | Build | Python |
| 26 | [Skill Permissions, Sandboxes, and Trust](phases/13-tools-and-protocols/26-skill-permissions-sandboxes-and-trust/) | Build | Python |
| 27 | [Skill Evals, Packaging, and Portability](phases/13-tools-and-protocols/27-skill-evals-packaging-and-portability/) | Build | Python |
| 28 | [MCP Tool Contracts and Content](phases/13-tools-and-protocols/28-mcp-tool-contracts-and-content/) | Build | Python |
| 29 | [MCP Reliability, Cancellation, and Flow Control](phases/13-tools-and-protocols/29-mcp-reliability-cancellation-and-flow-control/) | Build | Python |
| 30