The Kinds of Machine Learning

Lesson outline: The Kinds of Machine Learning

01Three questions, three kinds of answer
02Three label columns and their distinct values
03Choosing the kind from the column's data type
04Supervised learning: regression and classification
05Unsupervised learning: clustering, anomaly detection and dimensionality reduction
06Sorting ten real questions
07One column, two kinds of problem
08The code
09Recap, your challenge, and what comes next

This lesson stands on its own. These go deeper into what it uses.

The words this lesson introduces, each in one line. The module’s glossary collects them all, and the AI glossary has every term across the courses.

supervised learning: Learning from examples whose labels are known.
regression: Supervised learning where the answer is a quantity, so being close counts.
classification: Supervised learning where the answer is one of a set of categories.
binary: Classification with two classes, such as spam or ham.
multi-class: Classification with more than two classes, such as the ten digits.
unsupervised learning: Finding structure in examples that have no labels.
clustering: Finding groups of similar examples (Module 12).
anomaly detection: Finding unusual examples (Module 13).
dimensionality reduction: Describing examples with fewer columns (Module 11).
semi-supervised learning: Learning from a few labelled and many unlabelled examples.
self-supervised learning: Learning from labels taken from the data itself, as a language model predicts the next word.
reinforcement learning: Learning which actions to take from rewards; outside this track.

Your first pick on each question is the one that counts. Getting one wrong here is how the lesson sticks.