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.

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.

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 &amp; 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 — read. Open any completed lesson on aiengineeringfromscratch.com or expand a phase under Contents. No setup, no cloning.

Option B — 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

Option C — find your level (recommended). Skip ahead intelligently. Inside Claude, Cursor, Codex, OpenClaw, Hermes, or any agent with the curriculum skills installed:

/find-your-level

Ten questions. Maps your knowledge to a starting phase, builds a personalized path with hour estimates. After each phase:

/check-understanding 3        # quiz yourself on phase 3
ls phases/03-deep-learning-core/05-loss-functions/outputs/
# ├── prompt-loss-function-selector.md
# └── prompt-loss-debugger.md

Prerequisites

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

Built-in agent skills (Claude, Cursor, Codex, OpenClaw, Hermes)

Skill What it does
/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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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.

Install the lot with python3 scripts/install_skills.py. Real tools, not homework. By the end of the curriculum, you have a portfolio of 503 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.

code/agent_loop.py   build it

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](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/**call.args)
                history.append(tool_result(call.id, result))
            continue
        return msg.content
    raise StepLimitExceeded

outputs/skill-agent-loop.md   ship it

---
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

You are an agent debugger. Given the trace
of an agent run, identify the step where
the agent went wrong and explain why...

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Contents

Twenty phases. Click any phase to expand its lesson list.

Phase 0: Setup & Tooling 12 lessons

Get your environment ready for everything that follows.

# Lesson Type Lang
01 Dev Environment Build Python
02 Git & Collaboration Learn —
03 GPU Setup & Cloud Build Python
04 APIs & Keys Build Python
05 Jupyter Notebooks Build Python
06 Python Environments Build Shell
07 Docker for AI Build Docker
08 Editor Setup Build —
09 Data Management Build Python
10 Terminal & Shell Learn —
11 Linux for AI Learn —
12 Debugging & Profiling Build Python
# Lesson Type Lang
01 Linear Algebra Intuition Learn Python, Julia
02 Vectors, Matrices & Operations Build Python, Julia
03 Matrix Transformations & Eigenvalues Build Python, Julia
04 Calculus for ML: Derivatives & Gradients Learn Python
05 Chain Rule & Automatic Differentiation Build Python
06 Probability & Distributions Learn Python
07 Bayes' Theorem & Statistical Thinking Build Python
08 Optimization: Gradient Descent Family Build Python
09 Information Theory: Entropy, KL Divergence Learn Python
10 Dimensionality Reduction: PCA, t-SNE, UMAP Build Python
11 Singular Value Decomposition Build Python, Julia
12 Tensor Operations Build Python
13 Numerical Stability Build Python
14 Norms & Distances Build Python
15 Statistics for ML Build Python
16 Sampling Methods Build Python
17 Linear Systems Build Python
18 Convex Optimization Build Python
19 Complex Numbers for AI Learn Python
20 The Fourier Transform Build Python
21 Graph Theory for ML Build Python
22 Stochastic Processes Learn Python
# Lesson Type Lang
01 What Is Machine Learning Learn Python
02 Linear Regression from Scratch Build Python
03 Logistic Regression & Classification Build Python
04 Decision Trees & Random Forests Build Python
05 Support Vector Machines Build Python
06 KNN & Distance Metrics Build Python
07 Unsupervised Learning: K-Means, DBSCAN Build Python
08 Feature Engineering & Selection Build Python
09 Model Evaluation: Metrics, Cross-Validation Build Python
10 Bias, Variance & the Learning Curve Learn Python
11 Ensemble Methods: Boosting, Bagging, Stacking Build Python
12 Hyperparameter Tuning Build Python
13 ML Pipelines & Experiment Tracking Build Python
14 Naive Bayes Build Python
15 Time Series Fundamentals Build Python
16 Anomaly Detection Build Python
17 Handling Imbalanced Data Build Python
18 Feature Selection Build Python
# Lesson Type Lang
01 The Perceptron: Where It All Started Build Python
02 Multi-Layer Networks & Forward Pass Build Python
03 Backpropagation from Scratch Build Python
04 Activation Functions: ReLU, Sigmoid, GELU & Why Build Python
05 Loss Functions: MSE, Cross-Entropy, Contrastive Build Python
06 Optimizers: SGD, Momentum, Adam, AdamW Build Python
07 Regularization: Dropout, Weight Decay, BatchNorm Build Python
08 Weight Initialization & Training Stability Build Python
09 Learning Rate Schedules & Warmup Build Python
10 Build Your Own Mini Framework Build Python
11 Introduction to PyTorch Build Python
12 Introduction to JAX Build Python
13 Debugging Neural Networks Build Python
# Lesson Type Lang
01 Image Fundamentals: Pixels, Channels, Color Spaces Learn Python
02 Convolutions from Scratch Build Python
03 CNNs: LeNet to ResNet Build Python
04 Image Classification Build Python
05 Transfer Learning & Fine-Tuning Build Python
06 Object Detection — YOLO from Scratch Build Python
07 Semantic Segmentation — U-Net Build Python
08 Instance Segmentation — Mask R-CNN Build Python
09 Image Generation — GANs Build Python
10 Image Generation — Diffusion Models Build Python
11 Stable Diffusion — Architecture & Fine-Tuning Build Python
12 Video Understanding — Temporal Modeling Build Python
13 3D Vision: Point Clouds, NeRFs Build Python
14 Vision Transformers (ViT) Build Python
15 Real-Time Vision: Edge Deployment Build Python
16 Build a Complete Vision Pipeline Build Python
17 Self-Supervised Vision — SimCLR, DINO, MAE Build Python
18 Open-Vocabulary Vision — CLIP Build Python
19 OCR & Document Understanding Build Python
20 Image Retrieval & Metric Learning Build Python
21 Keypoint Detection & Pose Estimation Build Python
22 3D Gaussian Splatting from Scratch Build Python
23 Diffusion Transformers & Rectified Flow Build Python
24 SAM 3 & Open-Vocabulary Segmentation Build Python
25 Vision-Language Models (ViT-MLP-LLM) Build Python
26 Monocular Depth & Geometry Estimation Build Python
27 Multi-Object Tracking & Video Memory Build Python
28 World Models & Video Diffusion Build Python
# Lesson Type Lang
01 Text Processing: Tokenization, Stemming, Lemmatization Build Python
02 Bag of Words, TF-IDF & Text Representation Build Python
03 Word Embeddings: Word2Vec from Scratch Build Python
04 GloVe, FastText & Subword Embeddings Build Python
05 Sentiment Analysis Build Python
06 Named Entity Recognition (NER) Build Python
07 POS Tagging & Syntactic Parsing Build Python
08 Text Classification — CNNs & RNNs for Text Build Python
09 Sequence-to-Sequence Models Build Python
10 Attention Mechanism — The Breakthrough Build Python
11 Machine Translation Build Python
12 Text Summarization Build Python
13 Question Answering Systems Build Python
14 Information Retrieval & Search Build Python
15 Topic Modeling: LDA, BERTopic Build Python
16 Text Generation Build Python
17 Chatbots: Rule-Based to Neural Build Python
18 Multilingual NLP Build Python
19 Subword Tokenization: BPE, WordPiece, Unigram, SentencePiece Learn Python
20 Structured Outputs & Constrained Decoding Build Python
21 NLI & Textual Entailment Learn Python
22 Embedding Models Deep Dive Learn Python
23 Chunking Strategies for RAG Build Python
24 Coreference Resolution Learn Python
25 Entity Linking & Disambiguation Build Python
26 Relation Extraction & Knowledge Graph Construction Build Python
27 LLM Evaluation: RAGAS, DeepEval, G-Eval Build Python
28 Long-Context Evaluation: NIAH, RULER, LongBench, MRCR Learn Python
29 Dialogue State Tracking Build Python
# Lesson Type Lang
01 Audio Fundamentals: Waveforms, Sampling, FFT Learn Python
02 Spectrograms, Mel Scale & Audio Features Build Python
03 Audio Classification Build Python
04 Speech Recognition (ASR) Build Python
05 Whisper: Architecture & Fine-Tuning Build Python
06 Speaker Recognition & Verification Build Python
07 Text-to-Speech (TTS) Build Python
08 Voice Cloning & Voice Conversion Build Python
09 Music Generation Build Python
10 Audio-Language Models Build Python
11 Real-Time Audio Processing Build Python
12 Build a Voice Assistant Pipeline Build Python
13 Neural Audio Codecs — EnCodec, SNAC, Mimi, DAC Learn Python
14 Voice Activity Detection & Turn-Taking Build Python
15 Streaming Speech-to-Speech — Moshi, Hibiki Learn Python
16 Voice Anti-Spoofing & Audio Watermarking Build Python
17 Audio Evaluation — WER, MOS, MMAU, Leaderboards Learn Python
# Lesson Type Lang
01 Why Transformers: The Problems with RNNs Learn Python
02 Self-Attention from Scratch Build Python
03 Multi-Head Attention Build Python
04 Positional Encoding: Sinusoidal, RoPE, ALiBi Build Python
05 The Full Transformer: Encoder + Decoder Build Python
06 BERT — Masked Language Modeling Build Python
07 GPT — Causal Language Modeling Build Python
08 T5, BART — Encoder-Decoder Models Learn Python
09 Vision Transformers (ViT) Build Python
10 Audio Transformers — Whisper Architecture Learn Python
11 Mixture of Experts (MoE) Build Python
12 KV Cache, Flash Attention & Inference Optimization Build Python
13 Scaling Laws Learn Python
14 [Build a Transformer from Scratch](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/phases/07-transformers-deep-dive/14-buil