01 — Problem Framing
Translating a real-world task into a well-specified ML problem: defining input and output spaces, selecting loss functions, establishing baselines, and setting success criteria before any model is chosen.
Notes
- Problem Framing — supervised setup, problem types, loss function selection, baselines, success criteria, framing checklist