Supervised learning is the most commonly used ML approach, where models learn from labeled data. This means that each training sample consists of an input and a known correct output, allowing the algorithm to establish a relationship between them. The model is trained to map inputs to outputs by minimizing the error between predicted and actual values.
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Unsupervised learning is a type of ML where the model is given an unlabeled dataset and must identify patterns, structures, or relationships in the data without explicit guidance. Instead of learning from predefined outputs, the algorithm discovers hidden insights by grouping similar data points or reducing dimensionality.
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Semi-supervised learning is a hybrid approach that combines elements of both supervised and unsupervised learning. It is particularly useful when labeled data is scarce or expensive to obtain, but large amounts of unlabeled data are available. The model initially learns from a small set of labeled examples and then generalizes its knowledge to the larger, unlabeled dataset.
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