Build real-world machine learning skills using R—one of the most powerful tools for data analysis. This hands-on course from Geo-ICT teaches you how to explore data, build models, and uncover insights with machine learning techniques tailored for geo-information and geodata.
Machine learning with R
R ProgrammingR Programming
Machine learning with R
Machine learning is one of the most exciting technologies shaping the modern world. From recommending your next favorite movie to detecting diseases early, it’s everywhere—and growing fast. At its core, machine learning is a branch of artificial intelligence (AI) that enables computers to learn from data and make predictions without being explicitly programmed for each task.
R, a language widely used in data science and statistics, is especially powerful when it comes to analyzing and modeling complex data. With its flexibility, visual tools, and specialized libraries, R is ideal for those working with large datasets—particularly in fields like geo-information, where data can be layered, mapped, and explored in meaningful ways.
At Geo-ICT, we bring these two worlds together. This course is designed to teach you the fundamentals of machine learning while giving you practical experience using R. You’ll learn how to build and evaluate models, clean and transform data, and extract insights that support real decision-making. Whether you’re analyzing satellite imagery, predicting traffic patterns, or modeling climate trends, this course helps you use machine learning to bring your data to life.
Note: Some experience with R is required. Prefer working with Python? Check out our Machine Learning with Python course.
What will you learn
In this course, you’ll explore the foundations of machine learning through the lens of R programming. You’ll begin with a refresher on core R concepts, such as data frames, statistical functions, and essential libraries like tidyverse, caret, and ggplot2. Then, you’ll dive into the fundamentals of machine learning—learning what it is, how it works, and where it’s used.
You’ll cover both supervised and unsupervised learning. For supervised learning, you’ll learn to build models that make predictions from labeled data, such as regression and classification. For unsupervised learning, you’ll explore clustering techniques and dimensionality reduction to uncover patterns in unstructured data.
Throughout the course, you’ll get hands-on with real-world datasets and learn how to evaluate your models using tools like confusion matrices, ROC curves, and performance metrics such as accuracy and recall. You’ll also explore practical ways to clean, transform, and visualize your data to prepare it for machine learning tasks.
As you advance, you’ll gain experience with more complex models like decision trees, random forests, and ensemble methods. You’ll also get introduced to Spark for handling large datasets and Shiny, R’s web application framework, for building interactive dashboards to share your results.
Why choose this course
If you’re serious about learning machine learning and want to apply it to real-world data, especially in geo-information, this course offers the perfect combination of theory and practice.
- Expert guidance: Learn from instructors with real-world experience in data science and geospatial analytics.
- Project-based learning: Practice what you learn with hands-on exercises using real datasets and applications.
- Current tools and techniques: Work with the latest R libraries and stay up to date with modern machine learning workflows.
- Flexible, practical, and career-focused: Ideal for professionals and students looking to build practical machine learning skills in R.
Course structure
Day 1
We begin with a quick refresher on R programming—covering data types, frames, packages, and statistical functions. From there, you’ll be introduced to machine learning concepts, including model-based learning, supervised vs. unsupervised learning, and key tasks like classification, regression, clustering, and dimensionality reduction.
Day 2
You’ll learn how to evaluate and improve your models. Topics include correlation analysis, R-squared, F-tests, and residual analysis. You’ll explore regression models—linear, polynomial, and logistic—and evaluate them using graphical tools and performance metrics like accuracy, precision, and recall. You’ll also dive into functional programming with purrr, using map functions and combining them with dplyr for powerful data manipulation.
Day 3
The final day covers advanced topics. You’ll work with Spark to process big data using dplyr and Spark SQL. You’ll explore R’s Shiny framework to build interactive data dashboards. Finally, you’ll take on ensemble learning with decision trees, random forests, and bagging methods—learning how to boost accuracy and handle complex data.
Learning Outcomes
- Understand key machine learning concepts and when to use supervised vs. unsupervised learning
- Work with essential R libraries to clean, visualize, and model data
- Build and evaluate machine learning models for classification, regression, and clustering
- Apply evaluation techniques like ROC curves, confusion matrices, and performance scoring
- Automate workflows using functional programming tools in R
- Analyze and visualize large-scale data with Spark and dplyr
- Create interactive dashboards and apps using R Shiny
- Use ensemble techniques like decision trees and random forests to improve model accuracy
More Information?
Do you have questions about the course content? Not sure if the course aligns with your learning objectives? Or would you prefer a private session or in-company training? We’re happy to assist—feel free to get in touch.
Frequently Asked Questions about Machine Learning with R
In this course you will learn how to apply supervised and unsupervised models with R, including the use of R data types, data frames, libraries, and statistical functions.
This course is ideal for both novice and experienced geo-specialists, professionals from other sectors who want to retrain, and employees of companies and educational institutions who want to expand their knowledge of machine learning and R.
You will learn to work with R software and various machine learning algorithms. The focus is on practical skills such as classification, regression, clustering and dimensionality reduction.
The course provides essential knowledge and skills in machine learning that are highly sought after in the geospatial sector, which can significantly enhance your career prospects.
Basic programming knowledge is recommended, but the course is designed to be accessible to participants from different backgrounds and experience levels.
The course lasts 3 days, during which you will work intensively on understanding and applying machine learning concepts with R.
The course covers topics such as R programming, R Data Types and Data Frames, statistical functions in R, R Data Files, R Packages, and R Algorithms.
After completing the course, you will be able to train and develop self-learning machines using R.
Yes, after the course you can still ask questions to the teacher via email for two weeks. For practical problems, Geo-ICT also offers Online Support with 1-on-1 tailor-made lessons.
Yes, it is possible to take the course online via Google Meet. You can decide per course day whether you want to be physically present or take the course online.