Dive into the first part of a three-part video series on Support Vector Machines, demystifying one of the most enigmatic methods in Machine Learning. Learn about basic concepts, Maximal Margin Classifiers, Soft Margins, Support Vector Classifiers, and gain intuition behind Support Vector Machines. Explore polynomial kernel functions, radial basis function (RBF) kernels, and understand the kernel trick. Conclude with a comprehensive summary of key concepts, setting the foundation for deeper exploration in subsequent parts of the series.
Overview
Syllabus
Awesome song and introduction
Basic concepts and Maximal Margin Classifiers
Soft Margins allowing misclassifications
Soft Margin and Support Vector Classifiers
Intuition behind Support Vector Machines
The polynomial kernel function
The radial basis function RBF kernel
The kernel trick
Summary of concepts
Taught by
StatQuest with Josh Starmer
