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R Programming for Data Analysis - Machine Learning

Length 1 day
Price NZD 608.70 exc GST

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

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:

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

R Programming at Lumify Work

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

View all R Programming 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 Programming for Data Analysis - Beginner and R Programming for Data Analysis - Intermediate courses or have basic familiarity with R
You will not be expected to code unassisted but will achieve better learning outcomes if you have a fundamental understanding of R syntax
Basic understanding of statistics (mean, median, standard deviation, variance)

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.