Support Vector Machines (SVMs) are supervised machine learning models used for classification and regression tasks. The core principle of SVMs is to find an optimal hyperplane that best separates data points of different classes in a high-dimensional feature space. For linearly separable data, the SVM identifies the hyperplane that maximizes the margin—the distance between the hyperplane and the closest data points from each class, known as support vectors. For non-linearly separable data, SVMs employ the "kernel trick," which implicitly maps the input data into a higher-dimensional space where a linear separation might be possible. Common kernel functions include polynomial, radial basis function (RBF), and sigmoid. This ability to handle complex decision boundaries and its strong theoretical foundation make SVMs powerful and widely used algorithms, particularly effective in scenarios with high-dimensional data and a relatively small number of samples.