Support Vector Machines (SVMs) from scratch, without using dedicated packages, for the classification of linearly and non-linearly separable data Explores the theory and practical implementation of SVMs. Includes the optimization of the dual formulation, the implementation of different kernels such as linear, polynomial, and Radial Basis Function (RBF) kernels, and adding slack variables.