What is the difference between supervised and unsupervised learning?

Supervised learning involves training a machine learning model on a labeled dataset, where the input data is paired with the correct output. The goal is to learn a mapping from the input to the output so that the model can make accurate predictions on new, unseen data.

Examples of supervised learning include

• image classification,
• speech recognition,
• regression analysis.

Unsupervised learning involves training a model on an unlabeled dataset, with the goal of discovering patterns or structure in the data.

Examples of unsupervised learning include

• clustering,
• dimensionality reduction,
• anomaly detection.