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Understanding Supervised and Unsupervised Learning

Supervised learning predicts outcomes using labeled data. Unsupervised learning uncovers patterns in raw, unlabeled data. Two methods, two powerful approaches!

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This document compares supervised and unsupervised learning. Supervised learning uses labeled data to train models for tasks like classification and regression, making it ideal for predictive modeling. Unsupervised learning, on the other hand, works with unlabeled data to discover patterns and relationships, often used for clustering and anomaly detection. Supervised learning provides defined outputs, while unsupervised learning offers insights into the structure of data without predefined results. The choice depends on the availability of labeled data and the type of problem being solved.

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