// Learn AI
An AI course from zero to the frontier
Pick a route for who you are, then follow 10 stages · 43 units. Every unit lists the algorithms, must-read papers, a project and a self-test.
43 unitsabout 385 hours in total223 algorithms with papers112 code & exercise links13 lessons written
01Choose a route
Each route picks the units you need, in order, with total hours and a weekly pace.
0210 stages
From AI literacy to the research frontier. Each step builds on the one before.
03Algorithms Atlas
Open the atlas →223 algorithms in 20 areas, each with its original paper, prerequisites and resources.
04AI papers
Paper radar →New papers every day, a searchable archive and the essentials in reading order.
05Hands-on: code, exercises & tutorials
Every unit links 2–3 free places to practise: browser demos, exercise sets, runnable notebooks and reference code.
06Lessons you can read now
Full lessons already written. The course units link to them.
- 01 What Is a Large Language Model? A plain-language explanation of LLMs: next-token prediction, parameters, training and limits. Beginner 15 min ✓
- 02 How Machines Learn: Loss Functions and Gradient Descent The core loop of all modern AI training explained with one tiny example. Beginner 18 min ✓
- 03 Neural Networks and Backpropagation Neurons, layers, activations and how errors flow backwards to update millions of weights. Intermediate 20 min ✓
- 04 Tokens and Embeddings: How Text Becomes Numbers Tokenisation (BPE), vocabularies and the vector space where meaning lives. Intermediate 15 min ✓
- 05 Transformers and Attention, Explained Self-attention, queries/keys/values, multi-head attention and the full decoder block. Intermediate 25 min ✓
- 06 How LLMs Learn: Pre-training, Fine-tuning and RLHF The three-stage recipe behind ChatGPT-style assistants, and newer methods like DPO. Intermediate 25 min ✓
- 07 What Do LLMs Learn Inside? Representations and Interpretability Features, superposition, circuits and world models — what we know and what we don't. Advanced 25 min ✓
- 08 How LLMs Generate Text: Decoding, KV Cache and Quantisation Sampling, temperature, the KV cache, batching and quantisation in one lesson. Intermediate 20 min ✓
- 09 RAG Explained: Retrieval-Augmented Generation Chunking, embeddings, vector search, re-ranking and grounded answers with citations. Intermediate 25 min ✓
- 10 How AI Agents Work: The Agent Loop Model + tools + loop: observe, think, act, and the patterns that make agents reliable. Intermediate 25 min ✓
- 11 Tool Calling and MCP (Model Context Protocol) How function calling works and how MCP standardises tools for every AI app. Intermediate 25 min ✓
- 12 How to Evaluate LLMs: Benchmarks, Arenas and Contamination What MMLU, SWE-bench and Chatbot Arena measure, and how to read leaderboards critically. Intermediate 20 min ✓
- 13 Prompt Injection and Tool Poisoning Why agents that read untrusted content can be hijacked, and the defences that work. Intermediate 20 min ✓
For AI and developers
- The whole curriculum is available as JSON with lesson URLs, status, audiences, linked software and sources.
- /api/learn.json · /api/learn/algorithms.json · /llms.txt
FAQ
Where should a complete beginner start learning AI?
Start with the Beginner path: “What Is a Large Language Model?”, then tokens and embeddings, Transformers and attention, and how LLMs learn. No coding is required; each lesson takes 15–25 minutes.
Do I need maths or programming to understand large language models?
Not for the beginner path. Developers benefit from basic Python; researchers should review vectors, probability and derivatives in the Math & Machine Learning Foundations module.
How is this different from other AI courses?
The curriculum covers the whole AI stack — from maths to agents, safety, hardware and business — and links every lesson to the software you can actually run and to current AI news. Each written lesson cites papers and official documentation.
Can AI agents read this curriculum?
Yes. /api/learn.json lists every module and lesson with URLs, status, audiences, linked software and sources, and /api/learn/<lesson>.json returns the lesson text in Markdown.