Machine Learning in R

Length 1 day
Price $745 inc GST

Course overview

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Why study this course

Machine learning is all about using data to uncover insights and make accurate predictions.

This course builds on your existing R knowledge and introduces key machine learning techniques, including cluster analysis and creating predictive models using random forests. You’ll also learn how to assess model accuracy and apply your findings to real-world decision-making. For those working with Power BI, the course includes a demonstration on how to integrate R scripts into your Power BI workflow.

Training is delivered by an experienced instructor and includes hands-on practice throughout the day.

Nexacu Public Schedule

Nexacu is part of the Lumify Group, offering you the largest public schedule of end user applications and professional development training in Australia, New Zealand, and the Philippines. You can now access the schedule of courses and book, by clicking on the button below.

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What you’ll learn

After completing this course, students will be able to:

Perform cluster analysis to generate insights from data
Create regression and classification models using random forests in R
Evaluate and interpret the accuracy of predictive models
Use models to support informed, data-driven decisions
Incorporate R scripts into Power BI for enhanced analytics

R Programming at Lumify Work

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

Nexacu, part of the Lumify Group, delivers our R Programming data analysis training courses.

View all R Programming at Lumify Work courses

Course subjects

Introduction

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

Prerequisites

You should have completed our R Beginner course or be familiar with R syntax.

A basic understanding of statistics such as mean, median, and standard deviation is also recommended.

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.