Discover how to turn data into smart, actionable insights using Python. In this hands-on course from Geo-ICT, you'll learn how to build, train, and evaluate machine learning models, and apply them to real-world geodata challenges using the Anaconda Python distribution.
Machine learning with Python
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Machine learning with Python
Machine learning is changing the way we understand and use data. From self-driving cars and recommendation systems to climate forecasting and urban planning, machine learning is at the heart of innovation. It’s a branch of artificial intelligence (AI) that helps computers recognize patterns, learn from data, and make decisions—without needing to be programmed step-by-step for every task.
At Geo-ICT, we focus on using machine learning to unlock insights in geodata. Whether you’re analyzing satellite images, tracking environmental changes, or predicting natural disasters, machine learning helps make sense of complex spatial data. These techniques allow us to solve real problems and make informed decisions based on patterns in large datasets.
In this course, you’ll dive into the fundamentals of machine learning using Python—one of the most popular programming languages in the world. Python’s simplicity and flexibility, along with its powerful libraries, make it the go-to language for machine learning projects. Using tools like Jupyter Notebook, Scikit-learn, and Matplotlib, you’ll learn how to turn data into practical, visual insights that help you better understand and shape the world around you.
Note: Basic knowledge of Python is required. If you’re new to Python, we recommend starting with our Python programming course. Prefer R? Check out our Machine Learning with R course.
What will you learn
You’ll start by learning the essential concepts that form the foundation of machine learning. This includes understanding what supervised and unsupervised learning are, how classification and regression work, and why things like overfitting and underfitting matter. With these concepts in place, you’ll begin training your own models and evaluating how well they perform.
The course focuses on building models using Python’s most popular tools. You’ll get hands-on with Anaconda and Jupyter Notebooks, learn how to prepare and clean datasets using Pandas, and apply machine learning algorithms with Scikit-learn. You’ll also visualize your data and results using Matplotlib and Seaborn, making it easier to understand what’s happening inside your models.
From clustering and dimensionality reduction to decision trees and K-nearest neighbors, you’ll explore the full range of core machine learning techniques. Everything is taught in a practical, step-by-step way, with real examples that are especially relevant for those working with geodata. You’ll also complete a final project that puts all your new skills to the test.
Why choose this course
Choosing the right course can make all the difference in how confidently and effectively you use machine learning. Here’s why Geo-ICT’s course stands out:
- Learn by doing: You won’t just study concepts—you’ll apply them through projects, challenges, and hands-on exercises using real datasets.
- Expert instructors: Learn from professionals with years of real-world experience applying machine learning in geo-information and data science.
- Career-focused content: Everything you learn is aligned with what employers expect from modern data and GIS professionals.
- Current tools and tech: Get familiar with the latest tools and libraries like Scikit-learn, Pandas, TensorFlow, and more.
Course structure
Day 1
You’ll begin by installing Anaconda, creating your first Jupyter Notebook, and exploring essential machine learning libraries. You’ll train a simple model on a dataset and visualize your results using Matplotlib.
Day 2
Dive into supervised learning. You’ll explore classification and regression models, understand what causes overfitting and underfitting, and work with algorithms like K-nearest neighbors (KNN) and decision trees. You’ll also learn how to evaluate your models and improve their performance.
Day 3
On the final day, you’ll take on new challenges with unsupervised learning. You’ll preprocess and scale your data, apply clustering algorithms, and wrap up with a final project that puts all your skills together in a practical scenario.
Learning Outcomes
- Learn the core concepts of machine learning and how to apply them using Python
- Understand the difference between supervised and unsupervised learning techniques
- Train and evaluate models for classification, regression, and clustering tasks
- Clean and prepare data using Python libraries like Pandas
- Visualize results using Matplotlib and Seaborn to understand model performance
- Identify and address overfitting and underfitting in your models
- Apply machine learning techniques to geodata and spatial problems
- Complete a real project to practice and demonstrate your new skills
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 Python
You will delve into the world of Machine Learning, learn about ‘supervised’ and ‘unsupervised’ models and how to train them with Python.
If you already have some experience with Python and are curious about ML, this course is for you! The course is suitable for both new talents in the geo-sector and experienced professionals.
You’ll get started with top Python libraries such as Scikit-Learn and Jupyter Notebook, and learn all about NumPy, SciPy, matplotlib and pandas.
The course lasts 3 days.
Definitely. After the course you can email all your questions to the teacher for 2 weeks.
Yes, you can attend the course both in person and online via Google Meet.
This course costs €1695 excluding VAT.
Yes, you get a 10% discount for 3 people and 15% for 4 people.
Don’t worry, you can always take a follow-up course or opt for our 1-on-1 Online Support.
Yes, upon successful completion of the course you will receive a certificate, which is valuable for your professional development in the geosector.