After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
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
Curriculum
Course outline
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.
- Sugar content of soft drinks50 XP
- Visualizing a sugar content dataset100 XP
- Identifying decision boundaries50 XP
- Find the maximal margin separator100 XP
- Visualize the maximal margin separator100 XP
- Generating a linearly separable dataset50 XP
- Generate a 2d uniformly distributed dataset.100 XP
- Create a decision boundary100 XP
- Introduce a margin in the dataset100 XP
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.
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.
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.
R
Support Vector Machines in R
Course
Complete
