Supervised learning and unsupervised learning are two primary approaches in machine learning. Supervised learning involves labeled data, where the algorithm is trained on input-output pairs to predict outcomes, such as in classification or regression tasks. Examples include spam email detection and house price prediction.

In contrast, unsupervised learning works with unlabeled data, identifying hidden patterns or structures, such as clustering or dimensionality reduction. Examples include customer segmentation and anomaly detection.

The key difference lies in the presence of labeled data: supervised learning requires it, while unsupervised learning does not. To master these techniques, enrolling in a machine learning course can provide in-depth knowledge and practical skills.
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