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SVMs were first introduced by Vapnik [31]. The technique uses what we call support vectors to distinguish between data points belonging to different classes. The method aims to find the hyperplane that will best distinguish (margin maximization) different classes from each other. In its simplest form, it distinguishes two-class spaces from each other with the help of two equations w^Tx + b = + 1 and w^Tx + b = -1. SVMs were first developed in accordance with linear classification and, later, kernel functions for nonlinear spaces were developed. Kernel functions express a transformation between linear and nonlinear spaces. There are types such as linear, polynomial, radial basis function, and sigmoid. Depending on the nature of the data used, kernel functions can be superior to each other.