0 - prerequisites and ML basics
01 - architecture
02 - cloud computing
03 - azure
ML basics
04 - machine learning basics
05 - training, validation, and testing
06 - overfitting, underfitting, and regularisation
07 - classification metrics
08 - regression metrics
09 - training
10 - production ML and the model lifecycle
11 - LLM fundamentals
12 - embeddings, vector search and RAG
13 - docker, CI/CD and infrastructure as code