Answer / Ahsan Khan

Supervised learning algorithms are models that learn from labeled data, meaning they have both input (features) and output (target variable or labels) for each training instance. The goal is to learn a mapping function to predict outputs for new, unseen inputs. On the other hand, unsupervised learning algorithms work with unlabeled data, and their main goal is to find patterns or structure in the data without explicit guidance.

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