R Programming
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 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.
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.
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.
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.
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.
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.
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.
We aim to make our courses accessible to as many people as possible. If the course fee is a concern, instead of registering directly, you can indicate on the registration form that you’d like to receive a quote tailored to your needs. In many cases, we can offer flexible solutions. For instance, we can adjust the course content for large groups, shorten the course based on existing knowledge, or offer daily rates to suit your requirements.
Yes, you can reach out to the instructor with questions for up to 2 weeks after the course. Since the instructor is likely teaching other courses, we recommend emailing your questions to info@geo-ict.com. We’ll forward your inquiry to the instructor, and you’ll receive a response within 24 hours. After the 2-week period, we recommend using our Personalized Online Support for continued assistance.
Yes, we offer on-site training regularly throughout the Netherlands. Our instructor will bring laptops for the participants, and all you need to do is arrange a suitable room at your location.
Please send your requirements to info@geo-ict.com, and we will provide a customized quote, which will include travel and accommodation costs. Once the quote is confirmed, our course coordinator will reach out to schedule the training days.
After each course, participants receive a link to our evaluation portal where you can share your feedback on what you liked and didn’t like. We strive to provide a great experience for all our participants, but if you have a complaint, please click on ‘Complaints Procedure’ in the portal. This document will guide you through the steps to take. Geo-ICT Training Center, Netherlands, is a member of the Dutch Council for Training and Education (NRTO), ensuring a fair and transparent process.
Courses are typically scheduled according to the Dutch time zone, with sessions running from 9:00 AM to 12:00 PM and 1:00 PM to 4:00 PM. For participants in different time zones, we adjust the course times in consultation with you.
