# Course Support Vector Machines in R

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

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This online course about Support Vector Machines in R covers a key part of what a future data analyst would require.

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.

Enroll now in this Support Vector Machines in R course, and don’t miss the opportunity of learning with the best, as Kailash Awati is. With 47 enriching exercises, 13 videos, and an estimated time of 4 hours to successfully end up the course, you will become one of the best.

Requisites before you start
Chapter 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.
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.
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.
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.
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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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#R #Python #MachineLearning #BigData #DataAnalysis