Support Vector Machines (SVMs) is a popular machine learning algorithm used for classification and regression
SVM work by finding the optimal boundary (hyperplane) between two classes in the input data.
The hyperplane is defined by a set of weights and biases that are learned during training.
SVMs aim to maximize the distance between the hyperplane and the closest data points (called support vectors).
SVMs can also handle non-linearly separable data using kernel functions.
SVMs are sensitive to the choice of hyperparameters, such as the kernel type and the regularisation parameter.
The application of SVM includes bioinformatics, finance, and natural language processing.
SVMs are a powerful tool for supervised learning tasks, but can be computationally expensive for large datasets.