We are currently accepting expressions of interest for this course. Please register your interest to be notified when registration opens in mid-November.
Machine learning is transforming the way we analyse data, make predictions, and uncover patterns in complex datasets. This hands-on introductory course provides a practical overview of machine learning using Python and the popular scikit-learn library.
Led by experts at Earlham Institute, this course offers the added benefit of sessions designed to help participants frame biological research questions in ways that are well-suited to machine learning approaches.
Designed for learners with basic Python experience, the course introduces the fundamental concepts behind machine learning and guides participants through a range of commonly used techniques, including regression, classification, clustering, dimensionality reduction, ensemble methods, and an introduction to neural networks. Participants will gain experience preparing data, training models, evaluating performance, and applying machine learning methods to real-world problems.
This course offers an accessible starting point for understanding how these techniques can be applied in practice.
By the end of the workshop, you will have a solid foundation in key machine learning concepts and the confidence to continue exploring more advanced methods and applications.
Who is this workshop for?
Anyone with a basic knowledge of Python who wants to learn how machine learning can be used to analyse data, build predictive models, and support research or decision-making. The course is particularly suitable for researchers and technical staff looking to add machine learning techniques to their computational toolkit.
Course prerequisites
A basic understanding of Python. You will need to know how to write a for loop, if statement, use functions, libraries and perform basic arithmetic. Either of the Software Carpentry Python courses cover sufficient background:
Understanding bash is an advantage to set up the software and follow this training course. If you’re unsure whether you have enough experience to participate in this workshop, please read over the course pre-requisites and installation/set up guide for each course:
