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Supervised Learning: In supervised learning, the model is trained on a labeled dataset, which means the input data already has the correct output. Goal: Learn a mapping from inputs to outputs. Examples: Classification (spam detection), Regression (house price prediction). Algorithms: Linear Regression, Random Forest, Support Vector Machines, etc. Unsupervised Learning: In unsupervised learning, the model is trained on unlabeled data — it must find patterns or structures on its own. Goal: Discover hidden patterns or groupings in data. Examples: Clustering (customer segmentation), Dimensionality reduction (PCA). Algorithms: K-Means, DBSCAN, PCA, Autoencoders.

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