Phase 1: Classification Algorithms · ~90 minutes · Python
Support Vector Machines
Find the widest street between two classes. That is the entire idea.
Hiring signal: Understanding of support vector machines internals
What you will learn
- Implement a linear SVM from scratch using hinge loss and gradient descent on the primal formulation
- Explain the maximum margin principle and identify support vectors from a trained model
- Compare linear, polynomial, and RBF kernels and explain how the kernel trick avoids explicit high-dimensional mapping
- Evaluate the tradeoff controlled by the C parameter between margin width and classification errors
Introduction
Type: Build Language: Python Prerequisites: Phase 1 (Lessons 08 Optimization, 14 Norms and Distances, 18 Convex Optimization) Time: ~90 minutes
Learning Objectives
- Implement a linear SVM from scratch using hinge loss and gradient descent on the primal formulation
- Explain the maximum margin principle and identify support vectors from a trained model
- Compare linear, polynomial, and RBF kernels and explain how the kernel trick avoids explicit high-dimensional mapping
- Evaluate the tradeoff controlled by the C parameter between margin width and classification errors
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