Supervised and unsupervised learning

In supervised learning, an algorithm learns to map the relationship between the inputs and the outputs based on a labeled dataset. A labeled dataset includes the input data (also known as features) and the corresponding output labels (also known as targets). Basically, the aim of supervised learning is to build a mapping function that can accurately predict the output for new data. Examples of supervised learning include classification and regression. Classification focuses on predicting a discrete label, while regression focusses on predicting a continuous quantity.

Unsupervised learning tries to teach an algorithm to identify patterns and structures in data without any prior knowledge of the correct labels or outputs. In unsupervised learning, the algorithm is trained to find patterns, groupings, or clusters within that data on its own. Some common examples of unsupervised learning include clustering, dimensionality reduction, and anomaly detection.

In summary, supervised learning requires labeled data with known outputs, whereas unsupervised learning requires unlabeled data without any known outputs. Supervised learning is more commonly used for prediction, classification, or regression tasks, while unsupervised learning is more commonly used for exploratory data analysis and discovering hidden patterns or insights in data.