ai engineering from scratch

Learn AI Engineering! Learn it. Build it. Ship it for others.

AI Engineering from Scratch

Implement model internals, retrieval pipelines, and agent runtimes. Test them, inspect failures, and keep the code and evaluation results.

Start learning · Choose a path · Try a lab · Build a project · Browse the curriculum

Free, open source, MIT. Learn on the website, with a coding agent, or by running local code.

542 lessons. 20 phases. Python, TypeScript, Rust, Julia.

Read in your language

🇬🇧 English · 🇨🇳 简体中文 · 🇹🇼 繁體中文（台灣） · 🇯🇵 日本語 · 🇰🇷 한국어 · 🇵🇹 Português · 🇧🇷 Português (Brasil) · 🇪🇸 Español · 🇩🇪 Deutsch · 🇫🇷 Français · 🇮🇹 Italiano · 🇳🇱 Nederlands · 🇵🇱 Polski · 🇨🇿 Čeština · 🇷🇴 Română · 🇭🇺 Magyar · 🇬🇷 Ελληνικά · 🇸🇪 Svenska · 🇩🇰 Dansk · 🇳🇴 Norsk · 🇫🇮 Suomi · 🇷🇺 Русский · 🇺🇦 Українська · 🇹🇷 Türkçe · 🇮🇱 עברית · 🇸🇦 العربية · 🇮🇷 فارسی · 🇮🇳 हिन्दी · 🇧🇩 বাংলা · 🇵🇰 اردو · 🇹🇭 ไทย · 🇻🇳 Tiếng Việt · 🇮🇩 Bahasa Indonesia · 🇵🇭 Tagalog

Learning paths

Route	Starting lesson
Model foundations	Setup and tooling
LLM systems	Prompt engineering
Agents and delivery	The agent loop

Compare career paths · Prerequisites and study time

Gradient descent

Twenty starting points follow gradient descent on a quadratic loss. The graph shows their positions and mean loss after each update.

Adjust the learning rate in the lesson · Compare GD, momentum, and Adam in code

Projects

Three projects with staged starters, reference implementations, and local graders. Run commands from the repository root after setup. Starters fail until you implement the stages.

01 · Retrieval Evaluation Lab · Python · Ranking metrics and regression checks

A candidate improves mean NDCG while one query ranks its most relevant evidence lower. Build a query-by-query comparison that reports the regression and can fail a release check.

Use Python 3.10+. Review RAG and model evaluation. Implement ranking validation, precision and recall, rank-sensitive metrics, then system comparison.

python3 scripts/project_test.py retrieval-evaluation-lab \
  --init learning-artifacts/retrieval-evaluation-lab
python3 scripts/project_test.py retrieval-evaluation-lab \
  --stage 1 --path learning-artifacts/retrieval-evaluation-lab --strict
python3 scripts/project_test.py retrieval-evaluation-lab \
  --all --path learning-artifacts/retrieval-evaluation-lab --strict

Keep: a reproducible comparison with per-query deltas and the judgments used to score them. Metrics describe those judgments; they do not establish answer correctness.

Start the project · Inspect the reference · Run on your own inputs

02 · Agent Trace Debugger · TypeScript · Trace parsing and timing

A supplied trace still takes 100 ms, but total token usage rises by 200 and one span starts failing. Separate overlapping child work from parent execution time, then produce a report that exposes the change.

Use Node.js 22.18+ and Python 3 for the grader. Implement JSONL parsing, parent validation, interval arithmetic, then an inspectable timeline.

python3 scripts/project_test.py agent-trace-debugger \
  --init learning-artifacts/agent-trace-debugger
python3 scripts/project_test.py agent-trace-debugger \
  --stage 1 --path learning-artifacts/agent-trace-debugger --strict
python3 scripts/project_test.py agent-trace-debugger \
  --all --path learning-artifacts/agent-trace-debugger --strict

Keep: the input trace, an HTML timeline, and a JSON regression report. Preserve exclusive per-span token counts so parent and child usage are not counted twice.

Start the project · Inspect the reference · Explore timing interactively

03 · Tool Call Firewall · Rust · Role checks and approval receipts

A write changes after review, or an approval is replayed. Validate the call envelope, check the caller's role and path, then consume an approval bound to the exact request and content.

Use Rust and Python 3.10+. Review tool schema design and security boundaries. The caller application supplies identity; the model proposes an operation.

python3 scripts/project_test.py tool-call-firewall \
  --init learning-artifacts/tool-call-firewall
python3 scripts/project_test.py tool-call-firewall \
  --stage 1 --path learning-artifacts/tool-call-firewall --strict
python3 scripts/project_test.py tool-call-firewall \
  --all --path learning-artifacts/tool-call-firewall --strict

Keep: an audit receipt showing the requested operation and the policy decision. Approvals are single-use within one invocation; this project does not provide persistent authorization or an operating-system sandbox.

Start the project · Inspect the reference · Explore approval boundaries

Browse all projects · Career practice guide

Choose how to learn

On the website

Open any completed lesson on aiengineeringfromscratch.com or expand a phase under Contents. No setup, no cloning.

With an AI tutor

If Node.js, npx, and a skill-capable coding agent are already installed, your coding agent can become your tutor. 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.

npx skills add rohitg00/ai-engineering-from-scratch

Choose the host and scope when the installer asks. Use start-learning in Codex, /start-learning in Claude Code, or ask your host to use the skill by name.

Tutor setup and host commands

Check the local requirements first:

node --version
npx --version
python3 --version

skills writes to the host and scope selected during installation, such as .claude/skills/, .cursor/skills/, .codex/skills/, or another supported skills folder. Verify that the selected host discovers that exact destination.

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.

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.
`learn-reverse-engineering`	Focused Reverse Engineering tutor. Creates `REVERSE-ENGINEERING-LEARNING.md`, teaches Phase 13 lessons 32 to 45 on programs you build yourself, and records lab 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.
`mcpa-certification`	MCPA tutor. Follows the 34-lesson `mcpa-f` route on the 2026-07-28 protocol; teaches each lesson; runs labs and the wire checker; administers the diagnostic and three 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.

Run local code

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 vectors.py 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.

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.

Choose a learning path

You do not need to scan 542 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	Dev Environment
I know Python and want math plus ML foundations	Phase 1: Math Foundations	Linear Algebra Intuition
I want to build production LLM applications	Phase 11: LLM Engineering	Prompt Engineering
I want to build agents	Phase 14: Agent Engineering	The Agent Loop
I want to use coding agents on real repositories	Agent-Assisted Engineering path	Agent-Assisted Engineering
I want to shape the right build before implementation	Product Judgment and Delivery path	Product Judgment and Delivery

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.

Focused MCP, Agent Skills, and Reverse Engineering paths

Your goal	Learn on GitHub	Learn on the website
I want to build with Model Context Protocol (MCP)	Model Context Protocol (MCP) route	Model Context Protocol (MCP) path
I want to write and ship Agent Skills	Focused Agent Skills route	Agent Skills path
I want to read compiled programs	Reverse Engineering route	Reverse Engineering path

Prerequisites and study time

Prerequisites

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

Where to start

Background	Start at	Estimated time
New to programming and AI	Phase 0 — Setup	~306 hours
Know Python, new to ML	Phase 1 — Math Foundations	~270 hours
Know ML, new to deep learning	Phase 3 — Deep Learning Core	~200 hours
Know deep learning, want LLMs and agents	Phase 10 — LLMs from Scratch	~100 hours
Senior engineer, only want agent engineering	Phase 14 — Agent Engineering	~60 hours
Only want to build production MCP systems	Model Context Protocol (MCP) path	~23 hours 15 min
Only want to build production Agent Skills	Agent Skills Engineering path	~9.5 hours
Only want to learn reverse engineering	Reverse Engineering path	~32.5 hours

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 &amp; 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 &amp; 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 &amp; Protocols"]
  P13 --> P14["Phase 14 — Agent Engineering"]
  P14 --> P15["Phase 15 — Autonomous Systems"]
  P15 --> P16["Phase 16 — Multi-Agent &amp; Swarms"]
  P14 --> P17["Phase 17 — Infrastructure &amp; Production"]
  P15 --> P18["Phase 18 — Ethics &amp; Alignment"]
  P16 --> P19["Phase 19 — Capstone Projects"]
  P17 --> P19
  P18 --> P19

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

Phase 0: Setup & Tooling `13 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
13	Python for AI Engineering	Build	Python

Phase 1 — Math Foundations 22 lessons The intuition behind every AI algorithm, through code.

#	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

Phase 2 — ML Fundamentals 21 lessons Classical ML — still the backbone of most production AI.

#	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
19	ML System Design — From Problem to Deployed Model	Build	Python
20	Monitoring Models in Production — Data Drift and Concept Drift	Build	Python
21	Feature Stores and Training-Serving Skew	Build	Python

Phase 3 — Deep Learning Core 13 lessons Neural networks from first principles. No frameworks until you build one.

#	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

Phase 4 — Computer Vision 28 lessons From pixels to understanding — image, video, 3D, VLMs, and world models.

#	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

Phase 5 — NLP: Foundations to Advanced 29 lessons Language is the interface to intelligence.

#	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

Phase 6 — Speech & Audio 17 lessons Hear, understand, speak.

#	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

Phase 7 — Transformers Deep Dive 16 lessons The architecture that changed everything.

#	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	Build	Python
15	Attention Variants — Sliding Window, Sparse, Differential	Build	Python
16	Speculative Decoding — Draft, Verify, Repeat	Build	Python

Phase 8 — Generative AI 15 lessons Create images, video, audio, 3D, and more.

#	Lesson	Type	Lang
01	Generative Models: Taxonomy & History	Learn	Python
02	Autoencoders & VAE	Build	Python
03	GANs: Generator vs Discriminator	Build	Python
04	Conditional GANs & Pix2Pix	Build	Python
05	StyleGAN	Build	Python
06	Diffusion Models — DDPM from Scratch	Build	Python
07	Latent Diffusion & Stable Diffusion	Build	Python
08	ControlNet, LoRA & Conditioning	Build	Python
09	Inpainting, Outpainting & Editing	Build	Python
10	Video Generation	Build	Python
11	Audio Generation	Build	Python
12	3D Generation	Build	Python
13	Flow Matching & Rectified Flows	Build	Python
14	Evaluation: FID, CLIP Score	Build	Python
19	Visual Autoregressive Modeling (VAR): Next-Scale Prediction	Build	Python

Phase 9 — Reinforcement Learning 12 lessons The foundation of RLHF and game-playing AI.

#	Lesson	Type	Lang
01	MDPs, States, Actions & Rewards	Learn	Python
02	Dynamic Programming	Build	Python
03	Monte Carlo Methods	Build	Python
04	Q-Learning, SARSA	Build	Python
05	Deep Q-Networks (DQN)	Build	Python
06	Policy Gradients — REINFORCE	Build	Python
07	Actor-Critic — A2C, A3C	Build	Python
08	PPO	Build	Python
09	Reward Modeling & RLHF	Build	Python
10	Multi-Agent RL	Build	Python
11	Sim-to-Real Transfer	Build	Python
12	RL for Games	Build	Python

Phase 10 — LLMs from Scratch 24 lessons Build, train, and understand large language models.

#	Lesson	Type	Lang
01	Tokenizers: BPE, WordPiece, SentencePiece	Build	Python, Rust
02	Building a Tokenizer from Scratch	Build	Python
03	Data Pipelines for Pre-Training	Build	Python
04	Pre-Training a Mini GPT (124M)	Build	Python
05	Distributed Training, FSDP, DeepSpeed	Build	Python
06	Instruction Tuning — SFT	Build	Python
07	RLHF — Reward Model + PPO	Build	Python
08	DPO — Direct Preference Optimization	Build	Python
09	Constitutional AI & Self-Improvement	Build	Python
10	Evaluation — Benchmarks, Evals	Build	Python
11	Quantization: INT8, GPTQ, AWQ, GGUF	Build	Python
12	Inference Optimization	Build	Python
13	Building a Complete LLM Pipeline	Build	Python
14	Open Models: Architecture Walkthroughs	Learn	Python
15	Speculative Decoding and EAGLE-3	Build	Python
16	Differential Attention (V2)	Build	Python
17	Native Sparse Attention (DeepSeek NSA)	Build	Python
18	Multi-Token Prediction (MTP)	Build	Python
19	DualPipe Parallelism	Learn	Python
20	DeepSeek-V3 Architecture Walkthrough	Learn	Python
21	Jamba — Hybrid SSM-Transformer	Learn	Python
22	Async and Hogwild! Inference	Build	Python
25	Speculative Decoding and EAGLE	Build	Python
34	Gradient Checkpointing and Activation Recomputation	Build	Python

Phase 11 — LLM Engineering 17 lessons Put LLMs to work in production.

#	Lesson	Type	Lang
01	Prompt Engineering: Techniques & Patterns	Build	Python
02	Few-Shot, CoT, Tree-of-Thought	Build	Python
03	Structured Outputs	Build	Python
04	Embeddings & Vector Representations	Build	Python
05	Context Engineering	Build	Python
06	RAG: Retrieval-Augmented Generation	Build	Python
07	Advanced RAG: Chunking, Reranking	Build	Python
08	Fine-Tuning with LoRA & QLoRA	Build	Python
09	Function Calling & Tool Use	Build	Python
10	Evaluation & Testing	Build	Python
11	Caching, Rate Limiting & Cost	Build	Python
12	Guardrails & Safety	Build	Python
13	Building a Production LLM App	Build	Python
14	Model Context Protocol (MCP)	Build	Python
15	Prompt Caching & Context Caching	Build	Python
16	Agent State Machines — Graphs, Nodes, Checkpoints	Build	Python
17	Agent Framework Tradeoffs	Learn	Python

Phase 12 — Multimodal AI 25 lessons See, hear, read, and reason across modalities — from ViT patches to computer-use agents.

#	Lesson	Type	Lang
01	Vision Transformers and the Patch-Token Primitive	Learn	Python
02	CLIP and Contrastive Vision-Language Pretraining	Build	Python
03	BLIP-2 Q-Former as Modality Bridge	Build	Python
04	Flamingo and Gated Cross-Attention	Learn	Python
05	LLaVA and Visual Instruction Tuning	Build	Python
06	Any-Resolution Vision — Patch-n'-Pack and NaFlex	Build	Python
07	Open-Weight VLM Recipes: What Actually Matters	Learn	Python
08	LLaVA-OneVision: Single, Multi, Video	Build	Python
09	Qwen-VL Family and Dynamic-FPS Video	Learn	Python
10	InternVL3 Native Multimodal Pretraining	Learn	Python
11	Chameleon Early-Fusion Token-Only	Build	Python
12	Emu3 Next-Token Prediction for Generation	Learn	Python
13	Transfusion Autoregressive + Diffusion	Build	Python
14	Show-o Discrete-Diffusion Unified	Learn	Python
15	Janus-Pro Decoupled Encoders	Build	Python
16	MIO Any-to-Any Streaming	Learn	Python
17	Video-Language Temporal Grounding	Build	Python
18	Long-Video at Million-Token Context	Build	Python
19	Audio-Language Models: Whisper to AF3	Build	Python
20	Omni Models: Thinker-Talker Streaming	Build	Python
21	Embodied VLAs: RT-2, OpenVLA, π0, GR00T	Learn	Python
22	Document and Diagram Understanding	Build	Python
23	ColPali Vision-Native Document RAG	Build	Python
24	Multimodal RAG and Cross-Modal Retrieval	Build	Python
25	Multimodal Agents and Computer-Use (Capstone)	Build	Python

Phase 13 — Tools & Protocols 45 lessons The interfaces between AI and the real world.

#	Lesson	Type	Lang
01	The Tool Interface	Learn	Python
02	Function Calling Deep Dive	Build	Python
03	Parallel and Streaming Tool Calls	Build	Python
04	Structured Output	Build	Python
05	Tool Schema Design	Learn	Python
06	MCP Fundamentals: Stateless Requests and JSON-RPC	Learn	Python
07	Building an MCP Server: Stateless Python and TypeScript	Build	Python, TypeScript
08	Building an MCP Client: Discovery, Routing, and Dual-Era Fallback	Build	Python
09	MCP Transports: stdio and Stateless Streamable HTTP	Learn	Python
10	MCP Resources and Prompts: Addressable Context for Stateless Servers	Build	Python
11	MCP Model Input: Sampling Migration and Stateless MRTR	Build	Python
12	Explicit Scope and Stateless Elicitation	Build	Python
13	MCP Tasks Extension: Durable Work on a Stateless Core	Build	Python
14	MCP Apps on the Stateless Protocol	Build	Python
15	MCP Security: Poisoned Metadata, Routing, and MRTR State	Learn	Python
16	MCP Authorization: CIMD, Issuer Binding, PKCE, and Step-Up	Build	Python
17	Stateless MCP Gateways and Registry Admission	Learn	Python
18	MCP Auth in Production: Issuer-Bound Enrollment and Tokens	Build	Python
19	A2A Protocol	Build	Python
20	[OpenTelemetr