Bioinformatics Live - Introduction to Machine Learning with Python Our next Bioinformatics Live session will be Introduction to Machine Learning with Python, where we’ll briefly go over what machine learning is and take a closer look at different machine learning approaches: supervised, unsupervised and reinforcement learning. We’ll then put theory into practice by building a simple supervised antimicrobial resistance (AMR) prediction model using scikit-learn.By the end of the session, you’ll have learned how to:Define machine learning, and supervised, unsupervised and reinforcement learning approaches.Build a simple supervised AMR prediction model in python.Understand what data you need to build a supervised model.Distinguish between training, validation and testing datasets.Explore different metrics for evaluating a model.Save your model and analysis to support reproducible research.❗ Please bring a laptop with Python and Jupyter Notebook installed. You can find installation instructions here: Jupyter Notebook & Python.Whether you’re completely new to machine learning or simply curious to learn more, we’d love to see you there! Nov 12 2026 13.00 - 13.00 Bioinformatics Live - Introduction to Machine Learning with Python Take a closer look at different machine learning approaches. Room 7.14, Michael Swann Building, King's Buildings Campus This article was published on Friday 9 October 2026
Bioinformatics Live - Introduction to Machine Learning with Python Our next Bioinformatics Live session will be Introduction to Machine Learning with Python, where we’ll briefly go over what machine learning is and take a closer look at different machine learning approaches: supervised, unsupervised and reinforcement learning. We’ll then put theory into practice by building a simple supervised antimicrobial resistance (AMR) prediction model using scikit-learn.By the end of the session, you’ll have learned how to:Define machine learning, and supervised, unsupervised and reinforcement learning approaches.Build a simple supervised AMR prediction model in python.Understand what data you need to build a supervised model.Distinguish between training, validation and testing datasets.Explore different metrics for evaluating a model.Save your model and analysis to support reproducible research.❗ Please bring a laptop with Python and Jupyter Notebook installed. You can find installation instructions here: Jupyter Notebook & Python.Whether you’re completely new to machine learning or simply curious to learn more, we’d love to see you there! Nov 12 2026 13.00 - 13.00 Bioinformatics Live - Introduction to Machine Learning with Python Take a closer look at different machine learning approaches. Room 7.14, Michael Swann Building, King's Buildings Campus This article was published on Friday 9 October 2026
Nov 12 2026 13.00 - 13.00 Bioinformatics Live - Introduction to Machine Learning with Python Take a closer look at different machine learning approaches.
