In short
Machine learning helps organizations recognize patterns in data and make predictions. In this course, you will discover how to use data from machines to predict malfunctions. This is called “predictive maintenance”: planning maintenance before something goes wrong, so you can limit downtime and waste.
The course is intended for professionals with a basic knowledge of Python who want to move on to data analysis and AI applications. You will follow online sessions and work on an individual project. You will apply supervised and unsupervised learning to a realistic dataset and build a portfolio that shows how you use machine learning to solve a specific business problem.
This course is suitable for professionals who already have some experience with programming in Python and want to expand their knowledge to data science and AI. You will work on a concrete practical project and develop skills that are directly applicable within your organization, for example in optimizing processes and preventing malfunctions. Whether you want to strengthen your career with up-to-date knowledge, take a step towards data analysis, or find practical solutions for predictive maintenance, this course will get you started right away.
After completing this course, you will be able to independently develop machine learning models and apply them to practical data. You will have mastered the basics of data analysis, supervised and unsupervised learning, and model optimization and evaluation. Your portfolio will demonstrate your ability to use machine learning for predictive maintenance and process optimization.
Data analysis and preparation, machine learning modeling, feature engineering and dimensionality reduction, hyperparameter tuning and evaluation, practical implementation.
Is it right for you?
This course is suitable for professionals who already have some experience with programming in Python and want to expand their knowledge to data science and AI. You will work on a concrete practical project and develop skills that are directly applicable within your organization, for example in optimizing processes and preventing malfunctions. Whether you want to strengthen your career with up-to-date knowledge, take a step towards data analysis, or find practical solutions for predictive maintenance, this course will get you started right away.
After this, you can
After completing this course, you will be able to independently develop machine learning models and apply them to practical data. You will have mastered the basics of data analysis, supervised and unsupervised learning, and model optimization and evaluation. Your portfolio will demonstrate your ability to use machine learning for predictive maintenance and process optimization.
Areas for development
Data analysis and preparation, machine learning modeling, feature engineering and dimensionality reduction, hyperparameter tuning and evaluation, practical implementation.
Structure and content
Curious about what you can expect? Here you can read about how the course is structured, which topics are covered and how you can combine the programme with your work and private life. This will allow you to immediately determine whether it fits your schedule and ambitions.
About the programme
Structure
The course consists of five online sessions of an hour and a half, spread over ten weeks. In between sessions, you will complete practice assignments and delve deeper into relevant sources. At the same time, you will work on your project portfolio, focusing on predictive maintenance.
Programme
You will gain a solid foundation in machine learning and learn how to apply these techniques in a practical context. The main topics are:
- Data analysis and preparation: You will learn how to explore, clean, and prepare datasets using techniques such as feature engineering and dimensionality reduction.
- Supervised and unsupervised learning: You will apply models such as regression, decision trees, random forests, k-means clustering, and more.
- Hyperparameter tuning and evaluation: You will learn how to optimize and evaluate models using relevant metrics.
- Practical predictive maintenance project: You will work with a realistic dataset to predict machine failures and identify clusters of critical devices.
At the end of the course, you will be able to independently develop a machine learning prototype and substantiate it with a portfolio that demonstrates your knowledge and skills.
Why this course?
- Practical application of machine learning: You train models on data that is comparable to maintenance data from practice.
- Flexible and online: You attend five online sessions and work at your own pace.
- Portfolio as end product: You conclude with a substantiated prototype and an overview of your choices.
- Focus on sustainability and innovation: You explore how predictive maintenance contributes to less downtime, less waste, and smarter use of energy and materials.
Working and learning
The total study load is 130 hours, spread over 10 weeks (13 hours per week). There are five online meetings of an hour and a half each. In between, you will work on your project and self-study, so that you can work step by step towards a working prototype and portfolio.
Planning
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Start date
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04-05-2026
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Price
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€3100.00
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Sessions
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5 sessions
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Location
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Online
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Availability
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available
Practical information
Before you start the course, you will naturally want to know how everything is organised. In this section, you will find all the practical information you need: from admission requirements to costs and subsidy options. You can read about how to apply and find answers to frequently asked questions.
None. However, basic knowledge of programming in Python is assumed.
Upon successful completion of the project, you will receive a certificate demonstrating that you have successfully completed the module. The project will be assessed based on a number of criteria that you must meet.
This course delivers immediate value for your organization because participants learn to apply machine learning to maintenance issues. They work on a practical assignment with a realistic dataset and develop a prototype for predictive maintenance, including evaluation and substantiation. The result is a concrete portfolio that can help identify patterns and improve decision-making around maintenance.
Admission requirements
None. However, basic knowledge of programming in Python is assumed.
Certificate
Upon successful completion of the project, you will receive a certificate demonstrating that you have successfully completed the module. The project will be assessed based on a number of criteria that you must meet.
For the employer
This course delivers immediate value for your organization because participants learn to apply machine learning to maintenance issues. They work on a practical assignment with a realistic dataset and develop a prototype for predictive maintenance, including evaluation and substantiation. The result is a concrete portfolio that can help identify patterns and improve decision-making around maintenance.
Just Transition Fund (JTF)
Zuyd Flex Tech is co-financed by the European Union through the Just Transition Fund (JTF). The fund supports regions in their transition towards a climate-neutral economy.
Would you like to read this again calmly?
The brochure contains all the details clearly laid out, so you can take your time to look through everything again.
Still unsure about applying?
One of our training advisors will be happy to contact you to answer your questions and work with you to determine whether the training program is right for you.
Digital Twin
Make complex processes transparent with a digital twin and use scenarios to support sustainable decisions. You will conclude with your own blueprint.
Prompt Engineering for Sustainable Organizations
Use generative AI wisely and carefully, paying attention to ethics, sustainability, and strategic choices within your organization.
