Kernel Trick: RBF, Polynomial, and Sigmoid
Why kernels work, RBF kernel gamma parameter, choosing kernels for different data shapes.
When Lines Are Not Enough
A linear SVM only draws straight boundaries. Many datasets are not linearly separable, a circle of one class surrounded by another cannot be split by a line. The kernel trick solves this.
Mapping to Higher Dimensions
If data is not separable in its original space, projecting it into a higher-dimensional space can make it separable. A curve in 2D can become a flat plane in 3D.
All lessons in this course
- SVM Theory: Margins and Support Vectors
- Kernel Trick: RBF, Polynomial, and Sigmoid
- SVMs for Classification with sklearn
- SVMs for Regression (SVR)