Applied machine learning using geotechnical data (May 2025)

Interested in machine learning and prefer an applied approach? Join our 4-session course and learn machine learning by completing all the typical steps in an end-to-end project using a geotechnical dataset. You will build a supervised prediction model using a tabular dataset.You can use this as a cookbook for your next ML project, whether it is for research, a business idea or just an idea you want to explore. Basic knowledge of machine learning and Python programming is required. We start by setting up a good project structure and establishing practical tools and practices. The course guides you through data exploration, processing, training, evaluation, and deployment of your model. Instead of coding solutions from scratch, we use well-known, tested, and established solutions. The course offers practical knowledge directly applicable to real machine learning model development. Following scientific principles, we focus on proper configuration, structure, and organisation of variables. Results from experiments are tracked, and promising models are saved. You will apply your new skills through homework exercises and several smaller tasks. Enrol in our course today and start developing your own machine learning models.

Content

Applied machine learning, implemented using Python 3.11. Organising variables/configuration using Hydra, and experimentation results using mlFlow. The Python packages Scikit-learn, Pandas, Numpy and Matplotlib will be central, with a final deployment of an application in Streamlit cloud. Topics include: quality controlling data, outlier analysis, feature selection, data balancing, metrics, pipelines, hyperparameter optimisation,  training and evaluation using cross-validation, deployment of trained model.