SVM for Beginners: Support Vector Machines in R Studio
Learn how to create effective support vector machine models in Rstudio to analyse data for machine learning and predictions.
This free online course teaches you all you need to know about support vector machines (SVM) and their role in machine learning. We explain how to create both classification and regression SVM models using the ?R? program in the RStudio environment. These include simple to advanced models that use linear and non-linear kernels. This course can help you to solve problems and predict future changes using machine learning.
What You Will Learn In This Free Course
Setting up RStudio and R Crash Course
In this module, you will earn the basics of R and RStudio, starting from the installation processes. You will get an overview of the RStudio environment, the R commands you can use to input data, and the packages and data available in R. You will also learn to create graphs, change colours, and save the graphs.
Basics of Machine Learning and SVM
This module explains the concept of Support Vector Machines (SVM), starting with the concept of machine learning and the steps to build a machine learning model. You will learn the importance of data pre-processing and classification using the Maximum Margin Classifier, of which the SVM is an extension. You will also learn the concepts of hyperplanes and kernels, which are vital to understanding data processing and machine learning algorithms.
Building SVM Models in R
This module takes you back to the RStudio, where you will learn how to create an SVM model in R. You will begin by loading the data, then training and testing the data to build the classification model. This will be followed by tuning the hyperparameters to get better results. You will also learn to build a regression model using the same data set but different variables.
Course assessment
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