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R Programming for Data Analysis - Machine Learning

  • Length 1 day
Course overview
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Why study this course

Machine Learning involves using a variety of techniques to build predictive models or extract insights from data. Our Machine Learning course builds on your basic knowledge of R and will provide you with an understanding of the machine learning process. You will learn how to:

  • perform cluster analysis

  • create regression and classification models with random forests in R

For students interested in using R scripts in Power BI, your trainer will demonstrate how we can incorporate these analyses into a Power BI workflow.

Nexacu Public Schedule

With Lumify Group's acquisition of Nexacu, we're pleased to now offer you the largest public schedule of end user applications training in Australia and New Zealand. As we move to consolidate our end user offering with Nexacu, as an interim measure you can now access the schedule of the most closely aligned courses and book, by clicking on the link below.

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What you’ll learn

After completing this course, students will be able to:

  • Assess the predictive accuracy of classification and regression models

  • Leverage models to make predictions to guide decision-making

  • Incorporate R scripts in a Power BI workflow

  • Generate insights from data using cluster analysis

  • Create predictive models from data using random forests

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R Programming at Lumify Work

Learn R programming to analyse, manipulate, and visualise data more effectively.

Course subjects


  • Introduction to machine learning

  • Supervised vs unsupervised learning

  • The machine learning process

Cluster analysis

  • Purpose of cluster analysis

  • Real-world applications

  • K-means

  • How the algorithm works

  •  Data preparation

  • How many clusters?

  • Performing k-means clustering in R

  • Customer segmentation with cluster analysis

Random forests

  • Classification vs regression trees 

  • Basics of tree-based models 

  • The bias-variance trade-off

  • From trees to (random) forests

  • Ensemble learning: bagging to reduce overfitting and improve predictive accuracy

  • The process of supervised machine learning

  • Feature engineering

  • Splitting data into training and test sets

  • Training the model

  • Improving the model

  • Using the model for prediction

  • Evaluating the final model

  • Classification vs regression metrics

  • The process of creating a random forest model

  • Random forests in R 

  • Classification tree and random forest classification model

  • Regression tree and random forest regression model

  • Improving the model

R Scripts in Power BI 

  • Why bring a machine learning model into Power BI?

  • Setting up

  • Cluster analysis in Power BI

  • Random forest models in Power BI

  • R visuals in Power BI


Terms & Conditions

The supply of this course by Lumify Work is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.

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