What is the difference between supervised and unsupervised learning?

Machine learning algorithms can be categorized into two main types: supervised and unsupervised learning. The primary difference between these two types of learning is the presence or absence of a target variable, which is the variable that we want to predict or classify.

In supervised learning, the algorithm is provided with a labeled dataset, which includes both input variables and their corresponding target variables. The algorithm learns from this labeled dataset to make accurate predictions on new, unseen data. For instance, a supervised learning algorithm can be trained on a dataset of images and their corresponding labels, and can use this training to classify new images.

On the other hand, unsupervised learning algorithms are provided with an unlabeled dataset, which means that there is no target variable that the algorithm is trying to predict. Instead, the algorithm tries to identify patterns and relationships within the data. For instance, an unsupervised learning algorithm can be trained on a dataset of customer transaction records to identify patterns in customer purchasing behavior.

Some common algorithms used in supervised learning include linear regression, decision trees, and support vector machines, while common algorithms used in unsupervised learning include k-means clustering, principal component analysis (PCA), and association rule learning.