<ul><li>Supervised learning involves training a model using labeled data, where each input has a corresponding output or label.</li><li>It is used for tasks like classification and regression, where precise predictions are needed.</li><li>Unsupervised learning works with unlabeled data and focuses on discovering patterns or structures within the data.</li><li>It is useful for tasks like clustering and dimensionality reduction, where specific outcomes are not predicted.</li></ul>

Difference between Supervised learning and Unsupervised learning

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