Support Vector Machine (SVM) is a supervised machine learning algorithm primarily used for classification tasks but can also be adapted for regression. The core idea behind SVM is to find the hyperplane that best separates data points of different classes in a high-dimensional space. SVM aims to maximize the margin—the distance between the hyperplane and the nearest data points from each class, known as support vectors. This characteristic makes SVM robust to overfitting, particularly in high-dimensional spaces. SVM is widely used in applications such as text classification, image recognition, and bioinformatics due to its effectiveness and versatility.