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Supervised learning involves training models using labeled data, where algorithms learn patterns from input-output pairs. This method is ideal for tasks like classification and regression, enabling accurate predictions based on previous examples.

In contrast, unsupervised learning focuses on discovering patterns in unlabeled data. Algorithms autonomously identify relationships and structures within datasets, such as clustering similar data points or reducing dimensions for deeper insights.

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