Supervised learning task that predicts which discrete category an input belongs to. Binary (yes/no, spam/not spam) or multiclass (dog/cat/bird). The output is a class label.
In AI-901: classic examples are spam detection, medical diagnosis (sick/healthy), or sentiment classification (positive/negative/neutral). It differs from regression in that the output is categorical, not a continuous number.
Classification (ML) is a concept that can show up on the exam. Check the full guide to see it in context.