Machine Learning in R - Training Course
Understand the machine learning process using R
- perform cluster analysis
- create regression and classification models with random forests in R
Machine Learning in R - Training Course
Learn the basic processes of machine learning using R programming. Led by an experienced data scientist.
We don't have courses scheduled in your city currently. Please join our remote course options or contact us to register your interest.
We don't have courses scheduled in your city currently. Please join our remote course options or contact us to register your interest.
We don't have courses scheduled in your city currently. Please join our remote course options or contact us to register your interest.
We don't have courses scheduled in your city currently. Please join our remote course options or contact us to register your interest.
We don't have courses scheduled in your city currently. Please join our remote course options or contact us to register your interest.
We don't have courses scheduled in your city currently. Please join our remote course options or contact us to register your interest.
Frequently Asked Questions
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.
Machine Learning in R Course Details
Machine Learning in R Course Details
Machine Learning in R Course Details
Machine Learning in R Course Details
Machine Learning in R Course Details
Machine Learning in R Course Details
Machine Learning in R Course Details
Machine Learning in R Course Details
Machine Learning in R Course Details
R Programming Course Outlines
Data Analytics City Pages
Skills Test
Contact Us
What do I need to know to attend?
- Attended our R programming Basics and Beginner courses or have 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 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 Course Content
- 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
phone
email
enquiry
