Kernel Functions in Support Vector Machine (SVM)

In this video, we explore Kernel Functions used in Support Vector Machines (SVM) to transform data into a higher-dimensional space for better processing. Kernel Functions such as Gaussian Kernel, Radial Basis Function (RBF), Sigmoid Kernel, and Polynomial Kernel are discussed for their applications in machine learning models. We provide code examples for implementing these kernels using the Sci-kit Learn library. These kernel functions allow SVM to perform complex data transformations, helping in classification tasks. Also, we highlight a Data Science Course to master machine learning and data visualization.

For more details, check out the full article: Major Kernel Functions in Support Vector Machine (SVM).