AI, Machine Learning & Generative AI
Artificial intelligence now shapes almost every product and job, and this domain is where you learn to build with it, not just talk about it. You work hands-on with the large language models, ML pipelines and generative tools leading tech teams rely on daily. Courses walk you from prompt design through fine-tuning models and shipping real AI systems into production. Whether starting fresh or adding AI to skills you already have, you finish with practical, provable experience.
- LLMs
- OpenAI
- Anthropic Claude
- LangChain
- NVIDIA AI
- MLOps
Domain Scope
Where Generative AI & ML fits in real work today
321+
Courses in this domain
#5
Of 20 by catalogue depth
8
Career tracks mapped
6
Core technologies covered
Depth against nearby domains
This domain and its closest neighbours out of 20, ranked by how many courses each catalogue carries. Longer bar means a deeper catalogue.
- Cloud & DevOps#4 · 334
- Generative AI & ML#5 · 321
- Networking#6 · 294
- Agile, Scrum & PMP®#7 · 289
Generative AI & ML holds 8% of the 20-domain catalogue.
How hands-on each technology gets
An editorial reading of how much of the work is keyboard-on time rather than concepts. Not a course count.
- LLMs60
- OpenAI50
- Anthropic Claude55
- LangChain70
- NVIDIA AI85
- MLOps65
Higher means more lab, build and troubleshooting work; lower means more concept, policy and design work.
Career Tracks
8 routes through this domain
Ordered foundational to specialised. Bar length is how much weight the track carries inside the domain — most learners complete one or two of these, not all eight.
- Generative AI FoundationsBeginner · 4–6 wks
- Prompt Engineering & LLM AppsBeginner · 3–5 wks
- Building with OpenAI & Claude APIsIntermediate · 4–6 wks
- RAG & Retrieval SystemsIntermediate · 5–7 wks
- Agentic AI & OrchestrationIntermediate · 5–8 wks
- Model Fine-Tuning & AdaptationAdvanced · 6–8 wks
- GPU-Accelerated AIAdvanced · 6–9 wks
- MLOps & Production DeploymentAdvanced · 5–7 wks
The path most learners take
Three steps in sequence, then one specialisation. Picking a single branch is normal — finishing all four is not the expectation.
Step 1
Generative AI Foundations
Step 2
Prompt Engineering & LLM Applications
Step 3
Building with Model APIs
Then one of
Before you start
What to have ready before the foundations track. Everything else is taught from scratch.
- Comfortable writing basic Python
- REST API familiarity helps, but is not required
- No prior ML background needed to start
- Linear algebra recommended before fine-tuning
Technology Reference
What you actually learn, tool by tool
- LLMs60Attention, tokenization, embeddings and decoding — PyTorch and HF Transformers
- OpenAI50Chat and Responses APIs, function calling, structured outputs
- Anthropic Claude55Messages API, native tool use, extended thinking, prompt caching
- LangChain70Chains, agents, memory and retrievers — LCEL, LangGraph, LangSmith
- NVIDIA AI85CUDA, multi-GPU training, fine-tuning with NeMo, TensorRT inference
- MLOps65Experiment tracking, CI/CD for models, containerized serving, drift monitoring
The full technology list for this domain is LLMs, OpenAI, Anthropic Claude, LangChain, NVIDIA AI & MLOps — the six above are the ones the catalogue goes deepest on.
Why This Domain, Right Now
Generative AI & ML is worth the hours
Roles built around LLMs and generative AI are among the fastest-growing in tech hiring, and most postings now expect hands-on experience with a model API plus an orchestration framework — not just theory.
321+
Courses in this domain
#5
Of 20 by catalogue depth
8
Career tracks mapped
6
Core technologies covered
Looking wider? Browse all 4,176+ courses across 20 domains.
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