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1
Awesome song and introduction
2
Basic concepts and Maximal Margin Classifiers
3
Soft Margins allowing misclassifications
4
Soft Margin and Support Vector Classifiers
5
Intuition behind Support Vector Machines
6
The polynomial kernel function
7
The radial basis function RBF kernel
8
The kernel trick
9
Summary of concepts
Description:
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.

Support Vector Machines Part 1 - Main Ideas

StatQuest with Josh Starmer
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