Introduction to Machine Learning for Water Professionals

Register for this blended course to gain a practical understanding of Machine Learning and take the first step towards developing your own data-driven model.

Blended course details


Module 1 | Machine Learning mindset and Data Analysis                             
Topics: Definition of Machine Learning (ML) with examples, Overview of ML models, Problem Framing, Exploratory Data Analysis


Module 2 | Classification models
Topics: Pre-processing, Model selection, Feature importance, Evaluation, Introduction to scikit-learn library


Module 3 | Regression models
Topics: Pre-processing, Model selection, Loss functions, Bias/variance tradeoff


Module 4 | Neural networks
Topics: Neural Network architecture, Activation functions, Gradient descent, Introduction to keras library


Module 5 | Working with time series
Topics: Temporal patterns and seasonality, Forecasting approaches

Module 6 | Working with images
Topics: I
mage pre-processing, Strategies for image classification and object detection



Paul is a remote sensing specialist at DHI with a background in physics and mathematics. He has been working at DHI since 2021, focusing on satellite data processing and machine learning.

Rocco has a PhD in data-driven modelling of urban drainage systems and has worked as a consultant for climate adaptation projects. His current focus at DHI is on machine learning and its potential applications in urban water management.


199 excl. VAT
  • 90-day access to all course material
  • One 1-hour session with a tutor
  • Training certificate

Register today!