In machine learning, most tasks can be easily categorized into one of two different classes: supervised learning problems or unsupervised learning problems. In supervised learning, data has labels or classes appended to it, while in the case of unsupervised learning the data is unlabeled. Let's take a close look at why this distinction is important and look at some of the algorithms associated with each type of learning. Most machine learning tasks are in the domain of supervised learning. In supervised learning algorithms, the individual instances/data points in the dataset have a class or label assigned to them.