The Kernel Trick in Support Vector Machine (SVM)

SVM can only produce linear boundaries between classes by default, which not enough for most machine learning applications. In order to get nonlinear boundaries, you have to pre-apply a nonlinear transformation to the data. The kernel trick allows you to bypass the need for specifying this nonlinear transformation explicitly. Instead, you specify a "kernel" function that directly describes how each points relate to each other. Kernels are much more fun to work with and come with important computational benefits.


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Credit:
🐍 Manim and Python : https://github.com/3b1b/manim
🐵 Blender3D: https://www.blender.org/
🗒️ Emacs: https://www.gnu.org/software/emacs/

This video would not have been possible without the help of Gökçe Dayanıklı.