Learn
The concepts behind the problems
8 of 11 topics are written up as a full syllabus — every concept explained, with the trap that comes with it. The rest are coming soon.
7 topics
Coding
Write and run Python against real tests.
4 open · 3 soon751 concepts79 problems
Practise CodingNumPy
Vectorisation, broadcasting and linear algebra without Python loops.
Pandas
Reshaping, grouping, joining and cleaning real analytical data.
Machine Learning
Pipelines, validation and evaluation done without leaking labels.
Deep Learning
Layers, gradients and training loops implemented from scratch.
NLP
Tokenisation, embeddings, similarity and text classification.
LLM / GenAI
Chunking, retrieval, reranking and context construction in code.
AI Engineering
Batching, caching, retries and the plumbing around a model.
4 topics
System Design
Build production AI architectures on a canvas.
4 open1177 concepts39 problems
Practise System DesignML System Design
End-to-end machine learning systems at production scale.
LLM System Design
Gateways, routing, prompt management, guardrails and cost control.
RAG
Ingestion, retrieval, grounding and freshness over large corpora.
Agentic AI
Tools, memory, planning, multi-agent systems and safety, from first principles.