Skip to main content
HomeR

Course

Support Vector Machines in R

IntermediateSkill Level
4.8+
71 reviews
Updated 01/2023
This course will introduce the support vector machine (SVM) using an intuitive, visual approach.
Start Course for Free
RMachine Learning4 hr13 videos47 Exercises3,950 XP10,942Statement of Accomplishment

Create Your Free Account

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

Loved by learners at thousands of companies

Group

Training 2 or more people?

Try DataCamp for Business

Course Description

This course will introduce a powerful classifier, the support vector machine (SVM) using an intuitive, visual approach. Support Vector Machines in R will help students develop an understanding of the SVM model as a classifier and gain practical experience using R’s libsvm implementation from the e1071 package. Along the way, students will gain an intuitive understanding of important concepts, such as hard and soft margins, the kernel trick, different types of kernels, and how to tune SVM parameters. Get ready to classify data with this impressive model.

Prerequisites

Introduction to R
1

Introduction

This chapter introduces some key concepts of support vector machines through a simple 1-dimensional example. Students are also walked through the creation of a linearly separable dataset that is used in the subsequent chapter.
Start Chapter
2

Support Vector Classifiers - Linear Kernels

Introduces students to the basic concepts of support vector machines by applying the svm algorithm to a dataset that is linearly separable. Key concepts are illustrated through ggplot visualisations that are built from the outputs of the algorithm and the role of the cost parameter is highlighted via a simple example. The chapter closes with a section on how the algorithm deals with multiclass problems.
Start Chapter
3

Polynomial Kernels

Provides an introduction to polynomial kernels via a dataset that is radially separable (i.e. has a circular decision boundary). After demonstrating the inadequacy of linear kernels for this dataset, students will see how a simple transformation renders the problem linearly separable thus motivating an intuitive discussion of the kernel trick. Students will then apply the polynomial kernel to the dataset and tune the resulting classifier.
Start Chapter
4

Radial Basis Function Kernels

Builds on the previous three chapters by introducing the highly flexible Radial Basis Function (RBF) kernel. Students will create a "complex" dataset that shows up the limitations of polynomial kernels. Then, following an intuitive motivation for the RBF kernel, students see how it addresses the shortcomings of the other kernels discussed in this course.
Start Chapter
Support Vector Machines in R
Course
Complete

Earn Statement of Accomplishment

Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance reviewEnroll Now

Don’t just take our word for it

*4.8
from 71 reviews
86%
14%
0%
0%
0%
  • Napaporn
    yesterday

  • Samantha
    3 days ago

  • Mohammed
    last week

  • Takuya
    2 weeks ago

  • EDWIN SALIM
    2 weeks ago

  • Elaine
    3 weeks ago

Napaporn

Samantha

Mohammed

FAQs

Is this course suitable for beginners?

Absolutely! This course is designed to be easily accessible for beginners. It starts with the basics, introducing key concepts of support vector machines and providing a visual approach to learning.

Will I receive a certificate at the end of the course?

Yes, upon successful completion of the course, students will receive an electronic certificate.

What jobs would benefit from this course?

This course is suitable for a wide range of jobs, such as data scientists, machine learning engineers, data analysts, and software developers. It provides a great introduction to Support Vector Machines and its applications in R.

How is this course structured?

This course is divided into four chapters. The first chapter introduces some key concepts of support vector machines. The second chapter applies the svm algorithm to a dataset that is linearly separable. The third chapter introduces polynomial kernels via a dataset that is radially separable and the fourth chapter introduces the highly flexible Radial Basis Function (RBF) kernel.

What are the main concepts in Support Vector Machines?

The main concepts in SVM include hard and soft margins, the kernel trick, different types of kernels, and how to tune SVM parameters.

What packages does this course use?

This course uses R’s libsvm implementation from the e1071 package.

What tools are used to visually demonstrate the concepts of SVM?

This course uses ggplot visualisations to demonstrate the concepts of SVM.

How can I apply what I've learned in this course?

After completing this course, students will have the tools and knowledge to apply Support Vector Machines to solve problems using datasets.

    Join over 19 million learners and start Support Vector Machines in R today!

    Create Your Free Account

    or

    By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

    Grow your data skills with DataCamp for Mobile

    Make progress on the go with our mobile courses and daily 5-minute coding challenges.