Support Vector Machine (SVM) is a powerful supervised machine learning algorithm primarily used for classification and regression tasks. It works by finding the optimal hyperplane that separates data points of different classes in a high-dimensional space. SVM employs a technique known as the kernel trick, which allows it to handle non-linear relationships by transforming the input data into a higher-dimensional space. This capability makes SVM particularly effective for complex datasets. By focusing on the support vectors—data points that are closest to the hyperplane—SVM ensures robust classification performance, making it a popular choice in various applications, including image recognition and bioinformatics.