Supervised learning uses labeled data, like a student learning with a teacher. Unsupervised learning uses unlabeled data and is more like self-study.
Supervised learning knows the right answers in advance and aims for accuracy. It can classify data, like filtering spam, or predict values, like stock prices.
Unsupervised learning finds hidden patterns on its own. Think about grouping similar customers or discovering what products are often purchased together.
Semi-supervised learning combines both approaches, using a bit of labeled data to guide the learning from a larger set of unlabeled data. This can be useful for tasks like identifying medical conditions in scans where only a small number have been labeled by experts.
