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Introduction to machine learning

An inroduction to the course 'Machine Learning for Image Data', part of the DashDataCampp series. Presented by Andy French.

Welcome to the course: Machine Learning for Image Data.

The course is designed to take around four hours a week over five weeks to complete. It is aimed at bioscience professionals, particularly in the field of plant phenotyping, who are interested in learning more about machine learning using image data and how it can help their research.


All the practicals in the course are done using Python, in particular the machine learning package scikit-learn ( We’ll introduce scikit-learn in more detail later in the course, but if you feel you need more of an introduction to Python we recommend an earlier course in this series, Introduction to Image Analysis for Plant Phenotyping.

The people behind the course

Your lead educators are: Andrew French, Simon Parsons and Nathan Mellor.

We would also like to thank Sean Riley of Boardie Video Production for the video production itself.

Development and delivery of this course is supported by a UKRI Large-scale data training grant MR/V038850/1, “Data CAMPP (Innovative Training in Data Capture, Analysis and Management for Plant Phenotyping)”

More courses will follow over the next year to complement and extend this one, focusing on different aspects of data capture and analysis in plant phenotyping.

You might like to introduce yourself in the comments section below and let us know why you are taking the course, and what you are hoping to learn.

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Machine Learning for Image Data

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