ML foundations

The vocabulary and mental model behind every model.

Lessons

• What is machine learning? — Machine learning is programming with examples instead of hand-written rules.
• Supervised vs unsupervised — The first fork in ML: do your examples come with the right answer (a label), or not?
• Features & labels — Every supervised dataset is a table: input columns are features, the column you predict is the label.
• Train / test split — To know a model learned rather than memorised, test it on data it never saw in training.
• Foundations check — Lock in the core vocabulary before the boss: features, labels, supervision, splits.
• Foundations boss — Frame a real problem end to end. Clear it to unlock the Regression system.