SVM for Beginners: Support Vector Machines in R Studio

Learn Support Vector Machines in R Studio. Basic SVM models to kernel-based advanced SVM models of Machine Learning

4.55 (106 reviews)
Data Science
SVM for Beginners: Support Vector Machines in R Studio
5 hours
Jul 2022
last update
regular price

What you will learn

Get a solid understanding of Support Vector Machines (SVM)

Understand the business scenarios where Support Vector Machines (SVM) is applicable

Tune a machine learning model's hyperparameters and evaluate its performance.

Use Support Vector Machines (SVM) to make predictions

Implementation of SVM models in R programming language - R Studio


You're looking for a complete Support Vector Machines course that teaches you everything you need to create a SVM model in R, right?

You've found the right Support Vector Machines techniques course!

How this course will help you?

A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning advanced course.

If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the advanced technique of machine learning, which are Support Vector Machines.

Why should you choose this course?

This course covers all the steps that one should take while solving a business problem through SVM.

Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.

What makes us qualified to teach you?

The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course

We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones:

This is very good, i love the fact the all explanation given can be understood by a layman - Joshua

Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy

Our Promise

Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.

Download Practice files, take Quizzes, and complete Assignments

With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.

Go ahead and click the enroll button, and I'll see you in lesson 1!


Start-Tech Academy


Setting up R Studio and R Crash Course

Installing R and R studio
Course Resources
Basics of R and R studio
Packages in R
Inputting data part 1: Inbuilt datasets of R
Inputting data part 2: Manual data entry
Inputting data part 3: Importing from CSV or Text files
Creating Barplots in R
Creating Histograms in R

Machine Learning Basics

Introduction to Machine Learning
Building a Machine Learning Model

Maximum Margin Classifier

Course flow
The Concept of a Hyperplane
Maximum Margin Classifier
Limitations of Maximum Margin Classifier

Support Vector Classifier

Support Vector classifiers
Limitations of Support Vector Classifiers

Support Vector Machines

Kernel Based Support Vector Machines

Creating Support Vector Machine Model in R

The Data set for the Classification problem
Importing Data into R
Test-Train Split
Classification SVM model using Linear Kernel
Hyperparameter Tuning for Linear Kernel
Polynomial Kernel with Hyperparameter Tuning
Radial Kernel with Hyperparameter Tuning
The Data set for the Regression problem
SVM based Regression Model in R

Bonus Section

Bonus Lecture

Appendix 1: Preprocessing and Preparing Data before making any model

Gathering Business Knowledge
Data Exploration
The Data and the Data Dictionary
Importing the dataset into R
Univariate Analysis and EDD
EDD in R
Outlier Treatment
Outlier Treatment in R
Missing Value imputation
Missing Value imputation in R
Seasonality in Data
Bi-variate Analysis and Variable Transformation
Variable transformation in R
Non Usable Variables
Dummy variable creation: Handling qualitative data
Dummy variable creation in R
Correlation Matrix and cause-effect relationship
Correlation Matrix in R


September 24, 2021
I wish there was more about feature selection. However this was still a good course and would recommend.
June 13, 2020
Yes, nice course. More information about svm, use cases and logic behind would have maybe been better but this is a intro course so ok. Thanks!



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