Machine Learning in R Course - New Zealand
Understand the machine learning process using R
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 and
- 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. Training will be delivered by a member of our experienced team. Find more course details below.
Machine Learning in R Course - New Zealand
Learn the basic processes of machine learning using R programming. Led by an experienced trainer remotely in New Zealand.
Frequently Asked Questions
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.
"The course was really good, the trainer was able to answer my questions, the resources provided to help after the course are excellent. I was a bit overwhelmed at the beginning trying to learn a new language, but I was put at ease and went through the content at a pace that I could understand." - R Basics Brisbane, Australia
What is R?
R is an open source and free programming language that was developed for statistical analysis and 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 visualization.
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 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.
Machine Learning in R New Zealand Course Details
R Programming Course Outlines
R Programming Certification
Machine Learning in R Certification
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find out which course is right for you.
What do I need to know to attend?
- Attended our R programming Foundations and Beginner 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)
Machine Learning in R New Zealand Learning Outcomes
You will develop an understanding of and be able to:
- Generate insights from your data using cluster analysis
- Create predictive models from your data using random forests
- Assess the predictive accuracy of your classification and regression models
- Leverage models to make predictions to guide decision-making
- Incorporate R scripts in your Power BI workflow
Machine Learning in R New Zealand Course Content
- Introduction to machine learning
- Supervised vs unsupervised learning
- The machine learning process
- Cluster analysis
- Purpose of cluster analysis
- Real-world applications
- How the algorithm works
- Data preparation
- How many clusters?
- Performing k-means clustering in R
- Customer segmentation with cluster analysis
- Random forests
- Basics of tree-based models
- Classification vs regression trees
- From trees to forests
- Ensemble learning: bagging to reduce overfitting and improve predictive accuracy
- Preparing data for analysis
- Splitting data into training and test sets
- Training the model
- Assessing model accuracy
- Classification vs regression metrics
- Optimising the model
- Using the model for prediction
- R Scripts in Power BI
- Setting up
- Running R Scripts in Power Query to create new data
- Creating R visuals in Power BI
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