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Support Vector Machines in R

This course will introduce the support vector machine (SVM) using an intuitive, visual approach.

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4 Hours13 Videos47 Exercises7,837 Learners3950 XPMachine Learning Scientist TrackSupervised Machine Learning Track

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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.

  1. 1



    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.

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    Sugar content of soft drinks
    50 xp
    Visualizing a sugar content dataset
    100 xp
    Identifying decision boundaries
    50 xp
    Find the maximal margin separator
    100 xp
    Visualize the maximal margin separator
    100 xp
    Generating a linearly separable dataset
    50 xp
    Generate a 2d uniformly distributed dataset.
    100 xp
    Create a decision boundary
    100 xp
    Introduce a margin in the dataset
    100 xp
  2. 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.

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  3. 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.

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  4. 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.

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In the following tracks

Machine Learning ScientistSupervised Machine Learning


chesterChester IsmaybeccarobinsBecca Robins


Introduction to R
Kailash Awati Headshot

Kailash Awati

Senior Lecturer at University of Technology Sydney.

Kailash Awati is co-founder and principal of Sensanalytics, a consultancy specializing in sensemaking and analytics. He is also on the academic staff at the University of Technology Sydney where he teaches into the Master of Data Science and Innovation program. He blogs about analytics, sensemaking and his other professional interests at Eight to Late.
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