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Software used in the course

An overview of the software packages used in the course 'Machine learning for image data, including Python, Scikit-Learn, Matplotlib and Pandas
A montage of the logos for the Python packages Matplotlib, Pandas and Scikit-Learn, over some example data and plots
Here is a quick overview of the software we will use in the course.

All of the code used for the course is written using Python. If you haven’t already taken the course Introduction to Image Analysis for Plant Phenotyping we recommend doing so as many of the code builds on knowledge and skills developed there. If you’re new to Python we would particularly recommend the activity ‘Python Basics’ in Week 2 of that course.

Python packages

In this course, as well as the core Python language, we will use the following packages, which will be discussed in more detail in the following articles:

  • scikit-learn (essential): the machine learning toolkit itself
  • Matplotlib: plotting and visualisation
  • Pandas (optional): data manipulation.

All of these packages come pre-installed with the current distribution of Anaconda ( at the time of writing, so we would recommend installing Anaconda of you haven’t already.


In week five when we look at deep learning we will make use of interactive Python notebooks in Jupyter and Colab. Jupyter comes installed along with Anaconda, while Colab can be used purely within a web browser without the installation of any additional software.

Github repositiory

Throughout the course articles and practicals we will include snippets of code for you to try out for yourself, written in Python. Complete versions of all the code used in the course can be found at our Github repository:

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Machine Learning for Image Data

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