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 preapply 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.



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.