Machine learning has become an important tool for analysing biological and genomic data, helping researchers uncover patterns, make predictions, and gain new insights from complex datasets. From identifying cell types to predicting disease outcomes, these methods are increasingly used across modern life sciences. At the same time, applying machine learning in practice requires more than just theory: it involves choosing the right approach, working with real data, and understanding how to evaluate results.

This course provides a hands-on introduction to applying machine learning methods to biological data using Python. You will work with real-world datasets and learn how to build, evaluate, and improve models, from basic data handling with NumPy and Pandas to machine learning techniques and deep learning with PyTorch. The course also introduces reproducible workflows and modern computational approaches, including containerisation and GPU-accelerated analysis.