The Principles of Machine Learning​

This hands-on workshop introduces participants to the basic principle concepts, workflow, and practical application of Machine Learning (ML). Participants will learn key ML terminology, understand how models are built and evaluated, and apply popular ML algorithms to real datasets using Python. Using examples from clinical case studies, participants will explore data preparation, model selection, evaluation, overfitting/underfitting, and prediction.  

Recommend participants 

This workshop is ideal for anyone who work with data and are interested in applying machine learning in their research. It is suitable for beginners with basic Python or statistical knowledge, but no prior ML experience is required.  

Learning Outcomes 

By the end of the workshop, participants will be able to: 

• Understand basic machine learning concepts and terminology 
• Recognise common ML algorithms and their appropriate applications 
• Prepare and preprocess data for ML modelling 
• Build, train, and evaluate simple ML models 
• Interpret model performance using standard evaluation metrics 
• Identify common issues such as overfitting and underfitting 

Workshop Topics 

• Understanding the foundations of machine learning and types of learning 
• Exploring key algorithms such as regression, decision trees, and clustering 
• Preparing data through cleaning, visualisation, and preprocessing techniques 
• Building and evaluating ML models using Python or low-code tools 
• Interpreting model output and comparing performance across approaches