CHAPTER 12 / 40
Support Vector Machines
2 lessons · Explore, experiment and understand.
DOJO PETYour little coding companion
DOJO / YOUR GUIDE
Let’s figure it out.
Ask me about this course or where to start.
Chapter lessons
2 lessons
Why this matters in ML
SVMs find the maximum-margin hyperplane between classes — the geometric blueprint behind margin-based losses used today in hinge loss, contrastive learning, and metric learning.
- SVM
- kernel SVM
- hinge loss
- Step 52~20m3D · 12Code · 15★Most cinematic⚡Challenge
SVM: Separating Hyperplanes and Margins
Support Vector Machines (SVMs) solve binary classification by finding the maximum-margin hyperplane that separates two classes. Given labele…
Open lesson - Step 53~20m3D · 21Code · 15★Most cinematic⚡Challenge
SVM: Kernels
Linear SVMs can only draw straight decision boundaries — yet most interesting data is non-linearly separable. The kernel trick turns linear…
Open lesson