A Support Vector Machine (SVM) is a powerful machine learning algorithm used for classification tasks. It works by finding the best boundary, or hyperplane, that separates different classes in a dataset. The goal is to maximize the margin between the closest points of each class, known as support vectors. This helps the SVM make accurate predictions on new data. SVMs can handle both linear and non-linear data by using different kernel functions. For example, a linear kernel is used for linearly separable data, while a radial basis function (RBF) kernel can be applied to more complex datasets. This flexibility makes SVMs popular in various applications, from image recognition to text classification.