AI工程从零开始

Prerequisites

• You can write code (any language; Python helps).
• You want to understand how AI actually works, not just call APIs.

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.

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.

Phase 0: Setup & Tooling `12 lessons`

Get your environment ready for everything that follows.

<details id="phase-1"> <summary><b>Phase 1 — Math Foundations</b> &nbsp;<code>22 lessons</code>&nbsp; <em>The intuition behind every AI algorithm, through code.</em></summary> <br/>

</details>

<details id="phase-2"> <summary><b>Phase 2 — ML Fundamentals</b> &nbsp;<code>18 lessons</code>&nbsp; <em>Classical ML — still the backbone of most production AI.</em></summary> <br/>

</details>

<details id="phase-3"> <summary><b>Phase 3 — Deep Learning Core</b> &nbsp;<code>13 lessons</code>&nbsp; <em>Neural networks from first principles. No frameworks until you build one.</em></summary> <br/>

</details>

<details id="phase-4"> <summary><b>Phase 4 — Computer Vision</b> &nbsp;<code>28 lessons</code>&nbsp; <em>From pixels to understanding — image, video, 3D, VLMs, and world models.</em></summary> <br/>

</details>

<details id="phase-5"> <summary><b>Phase 5 — NLP: Foundations to Advanced</b> &nbsp;<code>29 lessons</code>&nbsp; <em>Language is the interface to intelligence.</em></summary> <br/>

</details>

<details id="phase-6"> <summary><b>Phase 6 — Speech & Audio</b> &nbsp;<code>17 lessons</code>&nbsp; <em>Hear, understand, speak.</em></summary> <br/>

</details>

<details id="phase-7"> <summary><b>Phase 7 — Transformers Deep Dive</b> &nbsp;<code>16 lessons</code>&nbsp; <em>The architecture that changed everything.</em></summary> <br/>

</details>

<details id="phase-8"> <summary><b>Phase 8 — Generative AI</b> &nbsp;<code>15 lessons</code>&nbsp; <em>Create images, video, audio, 3D, and more.</em></summary> <br/>

</details>

<details id="phase-9"> <summary><b>Phase 9 — Reinforcement Learning</b> &nbsp;<code>12 lessons</code>&nbsp; <em>The foundation of RLHF and game-playing AI.</em></summary> <br/>

</details>

<details id="phase-10"> <summary><b>Phase 10 — LLMs from Scratch</b> &nbsp;<code>24 lessons</code>&nbsp; <em>Build, train, and understand large language models.</em></summary> <br/>

</details>

<details id="phase-11"> <summary><b>Phase 11 — LLM Engineering</b> &nbsp;<code>17 lessons</code>&nbsp; <em>Put LLMs to work in production.</em></summary> <br/>

</details>

<details id="phase-12"> <summary><b>Phase 12 — Multimodal AI</b> &nbsp;<code>25 lessons</code>&nbsp; <em>See, hear, read, and reason across modalities — from ViT patches to computer-use agents.</em></summary> <br/>

</details>

<details id="phase-13"> <summary><b>Phase 13 — Tools & Protocols</b> &nbsp;<code>31 lessons</code>&nbsp; <em>The interfaces between AI and the real world.</em></summary> <br/>

FIG_002 · A worked sample

Phase 14, lesson 1: the agent loop. ~120 lines of pure Python, no dependencies.

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