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Would you like to learn how AI models make predictions based on data? In this course on machine learning, you will be working with Python, GenAI and predictive models. The Machine Learning module helps you analyse data more effectively and make better-informed decisions.

During this course, you learn how organisations use data and machine learning to make predictions and identify patterns. You are introduced to various machine learning methods and learn how to apply them using Python and modern AI tools. You work with models such as Linear Regression, Decision Tree and Random Forest.

The Machine Learning module combines theory with practical assignments. You not only learn how to build models, but also how to assess the quality of models and interpret the results. Topics such as model validation, classification and overfitting are covered.

In addition, you actively use GenAI to support your programming and analysis work. The module is structured in an accessible way, with plenty of room for coaching and asking questions. This way, you develop both technical skills and an understanding of machine learning and predictive analytics, step by step.

The knowledge gained can be put into practice straight away. You learn how to use data more effectively for analysis, forecasting and data-driven decision-making within your organisation. This lays a solid foundation for further specialisation in data analytics and AI.

Admission and application

Costs

One-time tuition fees academic year*€ 2.595

*You do not need to pay VAT on this module. Your organisation can claim the full cost of this degree programme as a business expense.

Check out our programme costs and funding page for more information about study costs and funding options.

Time investment

Duration

10 weeks

Start date

31 August 2026

Class days

3 hours per week on Monday from 6.00 PM to 9.00 PM

Independent study 

11 hours per week

Total workload

14 hours per week

Module content

What will you be doing?

Module

Practical information

Everything else you need to know

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