Supervised Learning vs Unsupervised Learning

Supervised Learning: In supervised learning, the algorithm is trained on a labeled dataset, where each input data point is paired with the correct output. The goal is for the algorithm to learn the mapping between inputs and outputs, making predictions on new unseen data based on this learned relationship.

Unsupervised Learning: Unsupervised learning, on the other hand, involves training the algorithm on an unlabeled dataset, where the algorithm is left to find patterns and relationships in the data on its own. This type of learning is used for tasks like clustering and dimensionality reduction.

Understanding the differences between supervised and unsupervised learning is crucial in machine learning as it determines the type of data the algorithm can work with and the type of problems it can solve.