Machine Learning in R - Training Course
Understand the machine learning process using R
Understand the machine learning process using R
5.00
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.
By the end of this course, participants will be able to:
You should have completed the R Beginner course or be familiar with R syntax. A basic understanding of statistics like mean, median, and standard deviation is also recommended.
We don't have courses scheduled in your city currently. Please join our remote course options or contact us to register your interest.
R BeginnerLearn the basic principles of R programming, the language for statistical and data analysis.
R IntermediateData manipulation, basic exploratory data analysis, customised data visualisations and basic modelling in R.
R AdvancedBuild on your base knowledge of the R language and learn how to run data analysis more efficiently.
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
Random forests (continued)
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
Course Introduction
Our R courses were designed by Tamara Shatar, who holds a PhD in Agricultural Data Science. She focused her extensive experience and skills in modelling using machine learning, simulation and other techniques to create a course with depth and applicability.
The course is consistently well reviewed by students.
"I found this 1 day course to be packed full of information - a very heavy load but masterfully delivered. It is very clear that we were learning from someone who is extremely capable and experienced with both the software solution AND it's real world application. I liken the experience to taking golf lessons from Tiger Woods or basketball coaching from Michael Jordan. All questions were addressed as if they were always part of the planned course content - seamless. Thank you so much for this opportunity." - R Programming Basics Sydney Remote
What is R?
R is an open source and free programming language that was developed for statistical analysis and the production of high-quality graphics. It has long been popular with statisticians and academics who make up part of the large active user community behind R. This community has contributed over 15,000 packages that extend the base functionality of R, making it easy to implement a vast range of techniques for data manipulation, analysis and visualisation.
What is Machine Learning?
Machine learning refers to a group of analysis techniques used to extract knowledge from data. It involves using mathematical or statistical models to predict outcomes. The models use algorithms (step-by-step programming instructions) to "learn" from data.
What is Remote Training?
Remote training at Nexacu means our team of experienced trainers will deliver your training live online. With remote learning, students can access our usual classroom training courses via video conferencing, ask questions, participate in the discussion, and share their screen with the trainer if they need help at any point. Students have the same level of participation and access to the trainer as they would in classroom training sessions.
R BeginnerLearn the basic principles of R programming, the language for statistical and data analysis.
R IntermediateData manipulation, basic exploratory data analysis, customised data visualisations and basic modelling in R.
R AdvancedBuild on your base knowledge of the R language and learn how to run data analysis more efficiently.
At Nexacu, we are proud to be the trusted training partner for hundreds of leading organisations across Australia, New Zealand, and around the world. From government agencies to multinational corporations, we help teams build practical skills and achieve real outcomes through expert-led training.
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Custom workbook included with a step by step exercises
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