Answer Posted / Ram Bhardwaj

Supervised learning involves training a model using labeled data, meaning the input data has predefined outputs or answers. The goal is to learn a mapping function between inputs and outputs so that the model can predict the output for new, unseen data. On the other hand, unsupervised learning works with unlabeled data where the model identifies patterns and structures in the data without any predefined labels.

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