What is the main difference between supervised and unsupervised learning?

Supervised learning and unsupervised learning are two primary categories of machine learning techniques that serve different purposes based on the nature of the data being analyzed.

In supervised learning, algorithms are trained on a labeled dataset, which means that the input data is paired with the correct output. For instance, in a classification task, a model learns to predict classes based on example inputs that have predefined labels. This structured approach enables the algorithm to make accurate predictions on new, unseen data since it can reference the learned labels from the training set.

On the other hand, unsupervised learning does not use labeled data. Instead, it works with datasets that contain only input features but no corresponding output labels. The goal here is to identify patterns or structures within the data, such as clustering similar items together or reducing dimensionality. Since there are no labels provided to guide the learning process, the models must extract insights based solely on the data itself.

The distinction lies in the requirement for labeled data: supervised learning needs it to train and validate models, while unsupervised learning operates without any labels, allowing it to uncover hidden structures or relationships within the dataset. This fundamental difference is crucial for understanding how each type of learning is applied and what kind of insights can be drawn from