Unsupervised Learning

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Unsupervised learning is a way to learn from data that has no labels or “correct answers.” Instead of predicting a target, the algorithm looks for structure in the dataset on its own. A common example is customer segmentation. If you have user behavior data but no predefined groups, unsupervised methods can cluster similar users together so teams can understand patterns and tailor strategies.

What makes unsupervised learning different from supervised learning is the absence of a target label. Supervised learning learns from labeled examples like “spam” or “not spam,” while unsupervised learning tries to discover patterns without being told what to look for. Since there is no ground truth to compare against, teams judge results by how meaningful the patterns are and whether the output is useful in a real workflow.

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