Supervised learning learns from examples that carry a correct answer, called a label. You hand it past emails marked spam or not spam, and it learns the mapping. Unsupervised learning gets rows with no answers attached at all. It looks for structure in the data itself, like groups of similar customers.
The practical split is usually about what you already have. Labels cost money, because someone has to read every email and mark it. When nothing is labeled, unsupervised methods still tell you something useful about the shape of your data.
Checking the result differs too. A supervised model has a right answer to score against, so error is a number. Unsupervised output needs a person to look at the clusters and decide whether they mean anything.
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