What you'll learn
- ✦Explain the difference between AI, ML, and LLMs in plain language
- ✦Describe how a large language model generates text (tokens, prediction, training)
- ✦Use AI coding assistants to write, explain, refactor, and debug code faster
- ✦Write prompts that give AI enough context to produce reliable, usable output
- ✦Recognise hallucinations and know when to verify AI output before trusting it
- ✦Apply responsible AI practices around privacy, bias, licensing, and cost
- ✦Call an LLM API, get structured output, and give a model your own data with RAG
- ✦Evaluate AI output, add guardrails, and understand agents and tools at a high level
Syllabus
Module 1
AI, ML & LLMs — the landscape Free
Module 2
Where AI fits in a developer's day Pro
Module 3
Working effectively with AI Pro
Module 4
Responsible & practical AI Pro
Module 5
Building with AI APIs Pro
Module 6
AI engineering practices Pro