ML Foundations
Questions and Python basics, then complete regression and classification workflows.
01 / Questions and tables
02 / Learning from examples
What fitting does
Recognise learned state from examples.
12–18 min · 3 practicesPredicting new rows
Preserve feature shape and meaning at prediction time.
12–18 min · 3 practicesMeasuring prediction error
Connect residuals and RMSE to target units.
12–18 min · 3 practicesFoundations retrieval
Retrieve earlier concepts before combining them.
15–20 min · 3 practices03 / Honest evaluation
Seen is not unseen
Explain why training performance is insufficient.
12–18 min · 3 practicesMaking a reproducible split
Protect evaluation rows before fitting.
12–18 min · 3 practicesPredicting classes
Distinguish class labels from quantities.
12–18 min · 3 practicesPreserving class representation
Apply and explain stratification.
12–18 min · 3 practicesCould we know this at prediction time?
Recognise leakage and context-dependent shortcuts.
12–18 min · 3 practicesHonest evaluation retrieval
Retrieve earlier concepts before combining them.
15–20 min · 3 practicesML Foundations checkpoint
Define X/y, split, fit, predict and evaluate a line.
25–35 min · 1 practices