Support Vector Machines in R

via

DataCamp

Course Features

Duration

4 hours

Delivery Method

Online

Available on

Limited Access

Accessibility

Mobile, Desktop, Laptop

Language

English

Subtitles

English

Level

Intermediate

Teaching Type

Self Paced

Video Content

4 hours

Course Description

This course introduces the powerful classifier, the support vector machine (SVM), through an intuitive and visual approach. Students will learn how support vector machines work in R. They'll also get to use the e1071 program R libsvm. Students will be able to understand concepts like hard and flexible margins, kernel tricks and different types of kernels. They also learn how to tune SVM parameters. This model allows you to classify data.

Course Overview

Virtual Labs

International Faculty

Post Course Interactions

Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Introduction to R

What You Will Learn

Introduces students to the basic concepts of support vector machines by applying the svm algorithm to a dataset that is linearly separable

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

Course Content

Expand all sections

Module 1: Introduction

Module 2: Support Vector Classifiers - Linear Kernels

Module 3: Polynomial Kernels

Module 4: Radial Basis Function Kernels

Course Instructors

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 t...