How does a Support Vector Machine (SVM) work for classification?

SVM is a supervised learning algorithm that finds the hyperplane that best separates the data points of different classes. The hyperplane maximizes the margin between the closest points of each class, known as support vectors. SVM can also use kernel functions to handle non-linearly separable data.