🧠 AI Engineering from Scratch

From linear algebra to autonomous agent swarms. learn AI with AI, then ship the tools.

🧭 Quick Navigation

🚀 Get Started  ·  🤖 AI-Native  ·  🗺️ The Journey  ·  🧰 Toolkit  ·  📚 Glossary  ·  🛣️ Roadmap  ·  🤝 Contribute  ·  🌐 Website

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💬 "84% of students already use AI tools. Only 18% feel prepared to use them professionally.

This course closes that gap."

283+ lessons. 20 phases. ~320 hours. From linear algebra to autonomous agent swarms. Python, TypeScript, Rust, Julia. Every lesson produces something reusable: prompts, skills, agents, and MCP servers.

You don't just learn AI. You learn AI with AI. Then you build real things. Then you ship tools others can use.

🆚 Why This Course?

📺 Traditional Courses	🧠 This Course
Scope One slice (NLP or Vision or Agents)	Scope 🌍 Everything — math · ML · DL · NLP · vision · speech · transformers · LLMs · agents · swarms
Languages Python only	Languages 🐍 Python · 🟦 TypeScript · 🦀 Rust · 🟣 Julia
Output "I learned something"	Output 📦 A portfolio of tools, prompts, skills, and agents you can install
Depth Surface-level or theory-heavy	Depth 🔬 Build from scratch first, then use frameworks
Format Videos you watch	Format 💻 Runnable code + docs + web app + AI-powered quizzes
Style Passive consumption	Style 🤖 AI-native — Claude Code skills test you as you go

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🤖 AI-Native Learning

This isn't a course you watch. It's a course you use with your AI coding agent.

🎯 Learn with AI, not just about AI

# 🧪 Find where to start based on what you already know
/find-your-level

# ✅ Quiz yourself after completing a phase
/check-understanding 3

# 📦 Every lesson produces a reusable artifact
ls phases/03-deep-learning-core/05-loss-functions/outputs/
# ├── prompt-loss-function-selector.md
# └── prompt-loss-debugger.md

🛠️ Built-in Claude Code Skills

🎴 Skill	⚡ What it does
	🧭 10-question quiz that maps your knowledge to a starting phase and builds a personalized path with hour estimates
	📝 Per-phase quiz (8 questions) with feedback and specific lessons to review

🚢 Every Lesson Ships Something

Other courses end with "congratulations, you learned X." Our lessons end with a reusable tool:

📝 Prompts Paste into any AI assistant for expert-level help

🎴 Skills Install into Claude Code, Cursor, or any agent

🤖 Agents Deploy as autonomous workers

🔌 MCP Servers Plug into any MCP-compatible AI app

277-term searchable glossary. Full lesson catalog. ~306 hours of content with per-lesson time estimates.
🌐 Browse the website →

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🗺️ The Journey

20 phases · 283+ lessons · click any phase to expand

Legend: hands-on implementation  ·  concept + intuition

12 lessons 🛠️ Get your environment ready for everything that follows. # Lesson Type Lang 01 Dev Environment 🐍 🟦 🦀 02 Git & Collaboration — 03 GPU Setup & Cloud 🐍 04 APIs & Keys 🐍 🟦 05 Jupyter Notebooks 🐍 06 Python Environments 🐍 07 Docker for AI 🐍 08 Editor Setup — 09 Data Management 🐍 10 Terminal & Shell — 11 Linux for AI — 12 Debugging & Profiling 🐍

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

#	Lesson	Type	Lang
01	Linear Algebra Intuition		🐍 🟣
02	Vectors, Matrices & Operations		🐍 🟣
03	Matrix Transformations & Eigenvalues		🐍 🟣
04	Calculus for ML: Derivatives & Gradients		🐍
05	Chain Rule & Automatic Differentiation		🐍
06	Probability & Distributions		🐍
07	Bayes' Theorem & Statistical Thinking		🐍
08	Optimization: Gradient Descent Family		🐍
09	Information Theory: Entropy, KL Divergence		🐍
10	Dimensionality Reduction: PCA, t-SNE, UMAP		🐍
11	Singular Value Decomposition		🐍 🟣
12	Tensor Operations		🐍
13	Numerical Stability		🐍
14	Norms & Distances		🐍
15	Statistics for ML		🐍
16	Sampling Methods		🐍
17	Linear Systems		🐍
18	Convex Optimization		🐍
19	Complex Numbers for AI		🐍
20	The Fourier Transform		🐍
21	Graph Theory for ML		🐍
22	Stochastic Processes		🐍

🔵 Phase 2 — ML Fundamentals  18 lessons  Classical ML — still the backbone of most production AI.

#	Lesson	Type	Lang
01	What Is Machine Learning		🐍
02	Linear Regression from Scratch		🐍
03	Logistic Regression & Classification		🐍
04	Decision Trees & Random Forests		🐍
05	Support Vector Machines		🐍
06	KNN & Distance Metrics		🐍
07	Unsupervised Learning: K-Means, DBSCAN		🐍
08	Feature Engineering & Selection		🐍
09	Model Evaluation: Metrics, Cross-Validation		🐍
10	Bias, Variance & the Learning Curve		🐍
11	Ensemble Methods: Boosting, Bagging, Stacking		🐍
12	Hyperparameter Tuning		🐍
13	ML Pipelines & Experiment Tracking		🐍
14	Naive Bayes		🐍
15	Time Series Fundamentals		🐍
16	Anomaly Detection		🐍
17	Handling Imbalanced Data		🐍
18	Feature Selection		🐍

🟢 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		🐍
02	Multi-Layer Networks & Forward Pass		🐍
03	Backpropagation from Scratch		🐍
04	Activation Functions: ReLU, Sigmoid, GELU & Why		🐍
05	Loss Functions: MSE, Cross-Entropy, Contrastive		🐍
06	Optimizers: SGD, Momentum, Adam, AdamW		🐍
07	Regularization: Dropout, Weight Decay, BatchNorm		🐍
08	Weight Initialization & Training Stability		🐍
09	Learning Rate Schedules & Warmup		🐍
10	Build Your Own Mini Framework		🐍
11	Introduction to PyTorch		🐍
12	Introduction to JAX		🐍
13	Debugging Neural Networks		🐍

🟠 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		🐍
02	Convolutions from Scratch		🐍
03	CNNs: LeNet to ResNet		🐍
04	Image Classification		🐍
05	Transfer Learning & Fine-Tuning		🐍
06	Object Detection — YOLO from Scratch		🐍
07	Semantic Segmentation — U-Net		🐍
08	Instance Segmentation — Mask R-CNN		🐍
09	Image Generation — GANs		🐍
10	Image Generation — Diffusion Models		🐍
11	Stable Diffusion — Architecture & Fine-Tuning		🐍
12	Video Understanding — Temporal Modeling		🐍
13	3D Vision: Point Clouds, NeRFs		🐍
14	Vision Transformers (ViT)		🐍
15	Real-Time Vision: Edge Deployment		🐍 🦀
16	Build a Complete Vision Pipeline		🐍
17	Self-Supervised Vision — SimCLR, DINO, MAE		🐍
18	Open-Vocabulary Vision — CLIP		🐍
19	OCR & Document Understanding		🐍
20	Image Retrieval & Metric Learning		🐍
21	Keypoint Detection & Pose Estimation		🐍
22	3D Gaussian Splatting from Scratch		🐍
23	Diffusion Transformers & Rectified Flow		🐍
24	SAM 3 & Open-Vocabulary Segmentation		🐍
25	Vision-Language Models (ViT-MLP-LLM)		🐍
26	Monocular Depth & Geometry Estimation		🐍
27	Multi-Object Tracking & Video Memory		🐍
28	World Models & Video Diffusion		🐍

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

#	Lesson	Type	Lang
01	Text Processing: Tokenization, Stemming, Lemmatization		🐍
02	Bag of Words, TF-IDF & Text Representation		🐍
03	Word Embeddings: Word2Vec from Scratch		🐍
04	GloVe, FastText & Subword Embeddings		🐍
05	Sentiment Analysis		🐍
06	Named Entity Recognition (NER)		🐍
07	POS Tagging & Syntactic Parsing		🐍
08	Text Classification — CNNs & RNNs for Text		🐍
09	Sequence-to-Sequence Models		🐍
10	Attention Mechanism — The Breakthrough		🐍
11	Machine Translation		🐍
12	Text Summarization		🐍
13	Question Answering Systems		🐍
14	Information Retrieval & Search		🐍
15	Topic Modeling: LDA, BERTopic		🐍
16	Text Generation		🐍
17	Chatbots: Rule-Based to Neural		🐍
18	Multilingual NLP		🐍
19	Subword Tokenization: BPE, WordPiece, Unigram, SentencePiece		🐍
20	Structured Outputs & Constrained Decoding		🐍
21	NLI & Textual Entailment		🐍
22	Embedding Models Deep Dive		🐍
23	Chunking Strategies for RAG		🐍
24	Coreference Resolution		🐍
25	Entity Linking & Disambiguation		🐍
26	Relation Extraction & Knowledge Graph Construction		🐍
27	LLM Evaluation: RAGAS, DeepEval, G-Eval		🐍
28	Long-Context Evaluation: NIAH, RULER, LongBench, MRCR		🐍
29	Dialogue State Tracking		🐍

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

#	Lesson	Type	Lang
01	Audio Fundamentals: Waveforms, Sampling, FFT		🐍
02	Spectrograms, Mel Scale & Audio Features		🐍
03	Audio Classification		🐍
04	Speech Recognition (ASR)		🐍
05	Whisper: Architecture & Fine-Tuning		🐍
06	Speaker Recognition & Verification		🐍
07	Text-to-Speech (TTS)		🐍
08	Voice Cloning & Voice Conversion		🐍
09	Music Generation		🐍
10	Audio-Language Models		🐍
11	Real-Time Audio Processing		🐍 🦀
12	Build a Voice Assistant Pipeline		🐍
13	Neural Audio Codecs — EnCodec, SNAC, Mimi, DAC		🐍
14	Voice Activity Detection & Turn-Taking		🐍
15	Streaming Speech-to-Speech — Moshi, Hibiki		🐍
16	Voice Anti-Spoofing & Audio Watermarking		🐍
17	Audio Evaluation — WER, MOS, MMAU, Leaderboards		🐍

🟢 Phase 7 — Transformers Deep Dive  14 lessons  The architecture that changed everything.

#	Lesson	Type	Lang
01	Why Transformers: The Problems with RNNs		🐍
02	Self-Attention from Scratch		🐍
03	Multi-Head Attention		🐍
04	Positional Encoding: Sinusoidal, RoPE, ALiBi		🐍
05	The Full Transformer: Encoder + Decoder		🐍
06	BERT — Masked Language Modeling		🐍
07	GPT — Causal Language Modeling		🐍
08	T5, BART — Encoder-Decoder Models		🐍
09	Vision Transformers (ViT)		🐍
10	Audio Transformers — Whisper Architecture		🐍
11	Mixture of Experts (MoE)		🐍
12	KV Cache, Flash Attention & Inference Optimization		🐍
13	Scaling Laws		🐍
14	Build a Transformer from Scratch		🐍

💗 Phase 8 — Generative AI  14 lessons  Create images, video, audio, 3D, and more.

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

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

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

🟧 Phase 10 — LLMs from Scratch  22 lessons  Build, train, and understand large language models.

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

🟥 Phase 11 — LLM Engineering  15 lessons  Put LLMs to work in production.

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

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

🟦 Phase 13 — Tools & Protocols  23 lessons  The interfaces between AI and the real world.

#	Lesson	Type	Lang
01	The Tool Interface		🐍
02	Function Calling Deep Dive		🐍
03	Parallel and Streaming Tool Calls		🐍
04	Structured Output		🐍
05	Tool Schema Design		🐍
06	MCP Fundamentals		🐍
07	Building an MCP Server		🐍
08	Building an MCP Client		🐍
09	MCP Transports		🐍
10	MCP Resources and Prompts		🐍
11	MCP Sampling		🐍
12	MCP Roots and Elicitation		🐍
13	MCP Async Tasks		🐍
14	MCP Apps		🐍
15	MCP Security I — Tool Poisoning		🐍
16	MCP Security II — OAuth 2.1		🐍
17	MCP Gateways and Registries		🐍
18	MCP Auth in Production — DCR + JWKS on iii		🐍
19	A2A Protocol		🐍
20	OpenTelemetry GenAI		🐍
21	LLM Routing Layer		🐍
22	Skills and Agent SDKs		🐍
23	Capstone — Tool Ecosystem		🐍

🟧 Phase 14 — Agent Engineering  30 lessons  Build agents from first principles — loop, memory, planning, frameworks, benchmarks, production.

#	Lesson	Type	Lang
01	The Agent Loop		🐍
02	ReWOO and Plan-and-Execute		🐍
03	Reflexion and Verbal Reinforcement Learning		🐍
04	Tree of Thoughts and LATS		🐍
05	Self-Refine and CRITIC		🐍
06	Tool Use and Function Calling		🐍
07	Memory — Virtual Context and MemGPT		🐍
08	Memory Blocks and Sleep-Time Compute		🐍
09	Hybrid Memory — Mem0 Vector + Graph + KV		🐍
10	Skill Libraries and Lifelong Learning — Voyager		🐍
11	Planning with HTN and Evolutionary Search		🐍
12	Anthropic's Workflow Patterns		🐍
13	LangGraph — Stateful Graphs and Durable Execution		🐍
14	AutoGen v0.4 — Actor Model		🐍
15	CrewAI — Role-Based Crews and Flows		🐍
16	OpenAI Agents SDK — Handoffs, Guardrails, Tracing		🐍
17	Claude Agent SDK — Subagents and Session Store		🐍
18	Agno and Mastra — Production Runtimes		🐍 🟦
19	Benchmarks — SWE-bench, GAIA, AgentBench		🐍
20	Benchmarks — WebArena and OSWorld		🐍
21	Computer Use — Claude, OpenAI CUA, Gemini		🐍
22	Voice Agents — Pipecat and LiveKit		🐍
23	OpenTelemetry GenAI Semantic Conventions		🐍
24	Agent Observability — Langfuse, Phoenix, Opik		🐍
25	Multi-Agent Debate and Collaboration		🐍
26	Failure Modes — Why Agents Break		🐍
27	Prompt Injection and the PVE Defense		🐍
28	Orchestration Patterns — Supervisor, Swarm, Hierarchical		🐍
29	Production Runtimes — Queue, Event, Cron		🐍
30	Eval-Driven Agent Development		🐍

🟩 Phase 15 — Autonomous Systems  22 lessons  Long-horizon agents, self-improvement, and the 2026 safety stack.

#	Lesson	Type	Lang
01	From Chatbots to Long-Horizon Agents (METR)		🐍
02	STaR, V-STaR, Quiet-STaR: Self-Taught Reasoning		🐍
03	AlphaEvolve: Evolutionary Coding Agents		🐍
04	Darwin Gödel Machine: Self-Modifying Agents		🐍
05	AI Scientist v2: Workshop-Level Research		🐍
06	Automated Alignment Research (Anthropic AAR)		🐍
07	Recursive Self-Improvement: Capability vs Alignment		🐍
08	Bounded Self-Improvement Designs		🐍
09	Autonomous Coding Agent Landscape (SWE-bench, CodeAct)		🐍
10	Claude Code Permission Modes and Auto Mode		🐍
11	Browser Agents and Indirect Prompt Injection		🐍
12	Durable Execution for Long-Running Agents		🐍
13	Action Budgets, Iteration Caps, Cost Governors		🐍
14	Kill Switches, Circuit Breakers, Canary Tokens		🐍
15	HITL: Propose-Then-Commit		🐍
16	Checkpoints and Rollback		🐍
17	Constitutional AI and Rule Overrides		🐍
18	Llama Guard and Input/Output Classification		🐍
19	Anthropic Responsible Scaling Policy v3.0		🐍
20	OpenAI Preparedness Framework and DeepMind FSF		🐍
21	METR Time Horizons and External Evaluation		🐍
22	CAIS, CAISI, and Societal-Scale Risk		🐍

🟩 Phase 16 — Multi-Agent & Swarms  25 lessons  Coordination, emergence, and collective intelligence.

#	Lesson	Type	Lang
01	Why Multi-Agent		🟦
02	FIPA-ACL Heritage and Speech Acts		🐍
03	Communication Protocols		🟦
04	The Multi-Agent Primitive Model		🐍
05	Supervisor / Orchestrator-Worker Pattern		🐍
06	Hierarchical Architecture and Decomposition Drift		🐍
07	Society of Mind and Multi-Agent Debate		🐍
08	Role Specialization — Planner / Critic / Executor / Verifier		🐍
09	Parallel Swarm and Networked Architectures		🐍
10	Group Chat and Speaker Selection		🐍
11	Handoffs and Routines (Stateless Orchestration)		🐍
12	A2A — The Agent-to-Agent Protocol		🐍
13	Shared Memory and Blackboard Patterns		🐍
14	Consensus and Byzantine Fault Tolerance		🐍
15	Voting, Self-Consistency, and Debate Topology		🐍
16	Negotiation and Bargaining		🐍
17	Generative Agents and Emergent Simulation		🐍
18	Theory of Mind and Emergent Coordination		🐍
19	Swarm Optimization (PSO, ACO)		🐍
20	MARL — MADDPG, QMIX, MAPPO		🐍
21	Agent Economies, Token Incentives, Reputation		🐍
22	Production Scaling — Queues, Checkpoints, Durability		🐍
23	Failure Modes — MAST, Groupthink, Monoculture		🐍
24	Evaluation and Coordination Benchmarks		🐍
25	Case Studies and 2026 State of the Art		🐍

⬛ Phase 17 — Infrastructure & Production  28 lessons  Ship AI to the real world.

#	Lesson	Type	Lang
01	Managed LLM Platforms — Bedrock, Azure OpenAI, Vertex AI		🐍
02	Inference Platform Economics — Fireworks, Together, Baseten, Modal		🐍
03	GPU Autoscaling on Kubernetes — Karpenter, KAI Scheduler		🐍
04	vLLM Serving Internals — PagedAttention, Continuous Batching, Chunked Prefill		🐍
05	EAGLE-3 Speculative Decoding in Production		🐍
06	SGLang and RadixAttention for Prefix-Heavy Workloads		🐍
07	TensorRT-LLM on Blackwell with FP8 and NVFP4		🐍
08	Inference Metrics — TTFT, TPOT, ITL, Goodput, P99		🐍
09	Production Quantization — AWQ, GPTQ, GGUF, FP8, NVFP4		🐍
10	Cold Start Mitigation for Serverless LLMs		🐍
11	Multi-Region LLM Serving and KV Cache Locality		🐍
12	Edge Inference — ANE, Hexagon, WebGPU, Jetson		🐍
13	LLM Observability Stack Selection		🐍
14	Prompt Caching and Semantic Caching Economics		🐍
15	Batch APIs — the 50% Discount as Industry Standard		🐍
16	Model Routing as a Cost-Reduction Primitive		🐍
17	Disaggregated Prefill/Decode — NVIDIA Dynamo and llm-d		🐍
18	vLLM Production Stack with LMCache KV Offloading		🐍
19	AI Gateways — LiteLLM, Portkey, Kong, Bifrost		🐍
20	Shadow, Canary, and Progressive Deployment		🐍
21	A/B Testing LLM Features — GrowthBook and Statsig		🐍
22	Load Testing LLM APIs — k6, LLMPerf, GenAI-Perf		🐍
23	SRE for AI — Multi-Agent Incident Response		🐍
24	Chaos Engineering for LLM Production		🐍
25	Security — Secrets, PII Scrubbing, Audit Logs		🐍
26	Compliance — SOC 2, HIPAA, GDPR, EU AI Act, ISO 42001		🐍
27	FinOps for LLMs — Unit Economics and Multi-Tenant Attribution		🐍
28	Self-Hosted Serving Selection — llama.cpp, Ollama, TGI, vLLM, SGLang		🐍

🟪 Phase 18 — Ethics, Safety & Alignment  30 lessons  Build AI that helps humanity. Not optional.

#	Lesson	Type	Lang
01	Instruction-Following as Alignment Signal		🐍
02	Reward Hacking & Goodhart's Law		🐍
03	Direct Preference Optimization Family		🐍
04	Sycophancy as RLHF Amplification		🐍
05	Constitutional AI & RLAIF		🐍
06	Mesa-Optimization & Deceptive Alignment		🐍
07	Sleeper Agents — Persistent Deception		🐍
08	In-Context Scheming in Frontier Models		🐍
09	Alignment Faking		🐍
10	AI Control — Safety Despite Subversion		🐍
11	Scalable Oversight & Weak-to-Strong		🐍
12	Red-Teaming: PAIR & Automated Attacks		🐍
13	Many-Shot Jailbreaking		🐍
14	ASCII Art & Visual Jailbreaks		🐍
15	Indirect Prompt Injection		🐍
16	Red-Team Tooling: Garak, Llama Guard, PyRIT		🐍
17	WMDP & Dual-Use Capability Evaluation		🐍
18	Frontier Safety Frameworks — RSP, PF, FSF		—
19	Model Welfare Research		🐍
20	Bias & Representational Harm		🐍
21	Fairness Criteria: Group, Individual, Counterfactual		🐍
22	Differential Privacy for LLMs		🐍
23	Watermarking: SynthID, Stable Signature, C2PA		🐍
24	Regulatory Frameworks: EU, US, UK, Korea		—
25	EchoLeak & CVEs for AI		🐍
26	Model, System & Dataset Cards		🐍
27	Data Provenance & Training-Data Governance		🐍
28	Alignment Research Ecosystem: MATS, Redwood, Apollo, METR		—
29	Moderation Systems: OpenAI, Perspective, Llama Guard		🐍
30	Dual-Use Risk: Cyber, Bio, Chem, Nuclear		—

🏆 Phase 19 — Capstone Projects  17 projects  2026 end-to-end shippable products, 20-40 hours each.

#	Project	Combines	Lang
01	Terminal-Native Coding Agent	P0 P5 P7 P10 P11 P13 P14 P15 P17 P18	🟦 🐍
02	RAG over Codebase (Cross-Repo Semantic Search)	P5 P7 P11 P13 P17	🐍 🟦
03	Real-Time Voice Assistant (ASR → LLM → TTS)	P6 P7 P11 P13 P14 P17	🐍 🟦
04	Multimodal Document QA (Vision-First)	P4 P5 P7 P11 P12 P17	🐍 🟦
05	Autonomous Research Agent (AI-Scientist Class)	P0 P2 P3 P7 P10 P14 P15 P16 P18	🐍
06	DevOps Troubleshooting Agent for Kubernetes	P11 P13 P14 P15 P17 P18	🐍 🟦
07	End-to-End Fine-Tuning Pipeline	P2 P3 P7 P10 P11 P17 P18	🐍
08	Production RAG Chatbot (Regulated Vertical)	P5 P7 P11 P12 P17 P18	🐍 🟦
09	Code Migration Agent (Repo-Level Upgrade)	P5 P7 P11 P13 P14 P15 P17	🐍 🟦
10	Multi-Agent Software Engineering Team	P11 P13 P14 P15 P16 P17	🐍 🟦
11	LLM Observability & Eval Dashboard	P11 P13 P17 P18	🟦 🐍
12	Video Understanding Pipeline (Scene → QA)	P4 P6 P7 P11 P12 P17	🐍 🟦
13	MCP Server with Registry and Governance	P11 P13 P14 P17 P18	🐍 🟦
14	Speculative-Decoding Inference Server	P3 P7 P10 P17	🐍
15	Constitutional Safety Harness + Red-Team Range	P10 P11 P13 P14 P18	🐍
16	GitHub Issue-to-PR Autonomous Agent	P11 P13 P14 P15 P17	🐍 🟦
17	Personal AI Tutor (Adaptive, Multimodal)	P5 P6 P11 P12 P14 P17 P18	🐍 🟦

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🧰 Course Output: The Toolkit

Other courses give you a certificate. This one gives you a toolkit.

Every lesson produces a reusable artifact — a prompt, skill, agent, or MCP server you can install and use immediately. By the end of the course you have:

outputs/
├── 📝 prompts/         Prompt templates for every AI task
├── 🎴 skills/          SKILL.md files for AI coding agents
├── 🤖 agents/          Agent definitions ready to deploy
└── 🔌 mcp-servers/     MCP servers you built during the course

💡 Install them with SkillKit. Plug them into Claude Code, Cursor, or any AI agent. These are real tools, not homework.

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📐 How Each Lesson Works

phases/XX-phase-name/NN-lesson-name/
├── 💻 code/           Runnable implementations (Python, TS, Rust, Julia)
├── 📖 docs/
│   └── en.md          Lesson documentation
└── 📦 outputs/        Prompts, skills, agents produced by this lesson

🔄 Every lesson follows 6 steps

Step	What happens
🎯 Motto	One-line core idea that sticks
❓ Problem	A concrete scenario where not knowing this hurts
🧠 Concept	Mermaid diagrams and intuition — no code yet
🔨 Build It	Implement from scratch in pure Python. No frameworks.
⚙️ Use It	Same thing with PyTorch, sklearn, or the real tool
🚢 Ship It	The prompt, skill, or agent this lesson produces

🔑 The Build It / Use It split is the key. You understand what the framework does because you built it yourself first.

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🚀 Getting Started

🅰️ Option A — Just start reading

Pick any completed lesson from the website or expand any phase above.

🅱️ 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) ⭐

If you already know some ML/DL, don't start from Phase 1. Use the built-in assessment:

# In Claude Code:
/find-your-level

This 10-question quiz maps your knowledge to a starting phase and builds a personalized path with hour estimates.

✅ Prerequisites

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

👤 Who This Is For

🧑‍💻 You are...	🚪 Start at...	⏱️ Time to complete
🌱 New to programming + AI	Phase 0 (Setup)	~306 hours
🐍 Know Python, new to ML	Phase 1 (Math)	~270 hours
📊 Know ML, new to DL	Phase 3 (Deep Learning)	~200 hours
🧠 Know DL, want LLMs/agents	Phase 10 (LLMs from Scratch)	~100 hours
🚀 Senior eng, want agents only	Phase 14 (Agent Engineering)	~60 hours

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📰 Why This Matters Now

📈 The Industry Signal "The hottest new programming language is English." — Andrej Karpathy (tweet) "Software engineering is being remade in front of our eyes." — Boris Cherny, creator of Claude Code "Models will keep getting better. The skill that compounds is knowing what to build." — Industry consensus, 2026

📚 Foundational Papers Covered 📄 Attention Is All You Need (Vaswani et al., 2017) → Phase 7 📄 GPT-3: Language Models are Few-Shot Learners → Phase 10 📄 Denoising Diffusion Probabilistic Models → Phase 8 📄 InstructGPT / RLHF → Phase 10 📄 Direct Preference Optimization (DPO) → Phase 10 📄 Chain-of-Thought Prompting → Phase 11 📄 ReAct: Reasoning + Acting in LLMs → Phase 14 📄 MCP: Model Context Protocol (Anthropic) → Phase 13

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🤝 Contributing

We welcome contributions of all kinds — new lessons, translations, fixes, and outputs.

📋 Want to...	👉 Read
Contribute a lesson or fix	CONTRIBUTING.md
Fork for your team or school	FORKING.md
See the lesson template	LESSON_TEMPLATE.md
Track progress	ROADMAP.md
Code of conduct	CODE_OF_CONDUCT.md

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⭐ Star History

🌟 If this helped you, please star the repo! It keeps the project alive.

💚 Built with care by Rohit Ghumare and the community.

_{📜 MIT License — Use it however you want. Fork it. Teach it. Sell it. Ship it.}

_{✨ From linear algebra to autonomous agent swarms — one lesson at a time. ✨}