Phase 1: Classification Algorithms · ~90 minutes · Python

Support Vector Machines

Find the widest street between two classes. That is the entire idea.

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: Earlier lessons in this course
Time: ~90 minutes

Objective

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

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

Create a free account — the opening phases of 29 of 35 courses are free, no credit card.

Key terms in this lesson

Gradient Descent