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Supervised learning involves training a model on labeled data, where the algorithm learns to map inputs to desired outputs. Unsupervised learning, on the other hand, deals with uncovering patterns and structures from unlabeled data, focusing on clustering and dimensionality reduction. Reinforcement learning revolves around an agent learning to make decisions in an environment to maximize cumulative reward.

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