Everyday Users

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1. What AI, ML, Deep Learning and LLMs Are

   Stage 0 · 3 h · 3 h total · lesson ready

   One map of the AI family, and how an LLM “predicts the next token”.

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2. Prompting & Using AI Well

   Stage 0 · 3 h · 6 h total

   Prompting techniques: role, context, examples, step-by-step thinking and structured output.

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3. Hallucination, Reliability & Limits

   Stage 0 · 2 h · 8 h total

   Why models confidently make things up, and how to check and reduce the risk.

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4. Tokenization & Embeddings

   Stage 4 · 6 h · 14 h total · lesson ready

   BPE tokenisation, per-token pricing, embeddings and semantic similarity.

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5. RAG Engineering & Evaluation

   Stage 8 · 8 h · 22 h total · lesson ready

   Chunking, query rewriting, HyDE, GraphRAG, citations and evaluation metrics.

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6. Agents: Tool Use, MCP, Planning & Memory

   Stage 8 · 12 h · 34 h total · lesson ready

   ReAct, function calling, MCP, reflection, memory, multi-agent systems, computer use and sandboxes.

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7. Security: Jailbreaks, Injection & Privacy

   Stage 9 · 6 h · 40 h total · lesson ready

   Adversarial examples, GCG, prompt injection, red-teaming, watermarking, differential privacy and backdoors.

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• 1Application Developers · Python is enough: in 3 months, ship a production-ready RAG / agent app you can evaluate and deploy.126h
• 2Algorithm Researchers · From maths to frontier papers: in 6–12 months, read and reproduce papers on LLMs, alignment and reasoning models.257h
• 3Product Managers & Leaders · Understand technical limits, cost structure and risk well enough to choose tools, start projects and set governance.84h
• 4Domain Scientists · With a background in your own field, learn to do research with ML and LLMs — linked with the “AI × Disciplines” section.143h