What is a Support Vector Machine (SVM) and how does it work?

A Support Vector Machine (SVM) is a supervised learning algorithm primarily used for classification tasks, though it can also be adapted for regression. It works by finding the optimal hyperplane that separates data into classes in the feature space. The hyperplane is defined by the support vectors, which are the data points closest to the hyperplane. SVM aims to maximize the margin between these support vectors and the hyperplane. In cases where classes are not linearly separable, SVM can use kernel functions to transform the data into a higher-dimensional space where a hyperplane can be used for separation.