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Machine Learning for Image Data

Master the principles and applications of machine learning for image data to harness its potential for plant phenotyping.

495 enrolled on this course

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Machine Learning for Image Data

495 enrolled on this course

  • 5 weeks

  • 3 hours per week

  • Digital certificate when eligible

  • Intermediate level

Find out more about how to join this course

  • Duration

    5 weeks
  • Weekly study

    3 hours
  • 100% online

    How it works
  • Unlimited subscription

    $244.99 for one whole yearLearn more

Become an expert in machine learning for bioscience

Machine learning has made it possible to process vast quantities of image data. That means it can enhance and facilitate the work of bioscience researchers, particularly the field of plant phenotyping.

On this five-week course from the University of Nottingham, you’ll gain an overview of the applications of machine learning for image data, focusing specifically on its use in plant phenotyping.

Gain an overview of machine learning as it applies to biological image data

You’ll start the course with an overview of machine learning, and an introduction to image data and features.

You’ll gain the background you need to understand and apply machine learning in your own bioscience research.

Master common techniques and softwares for image analysis

Once you’ve mastered the principles of machine learning for image data, you’ll start building the practical skills you need to navigate machine learning software.

Weeks 3 and 4 of the course will cover the main techniques for processing image data, some common challenges surrounding these, and useful tips and tricks to help you overcome them.

Whether you want to model data through a decision tree or create visualisations using Python, you’ll gain the hands-on experience you need for your research.

Understand neural networks and deep learning

In your last week of the course, you’ll look more closely at a specific subfield of machine learning: deep learning. You’ll learn how neural networks can be used to process biological images in the same way the human brain would.

By the end of the course, you’ll have an understanding of how machine learning can be used with biological image data, and the skills you need to harness it in your own bioscience research.

Syllabus

  • Week 1

    What is machine learning?

    • Introduction to machine learning

      An introduction to the course, and an introduction to image data and features.

    • Example machine learning problems

      A discussion of the main types of problems you might solve with machine learning, and the kinds of problems that are specific to image data. We also look at classification and clustering applications with a set of example data.

    • Supervised versus unsupervised learning

      To tackle any problem using machine learning you need to establish whether supervised or unsupervised learning is appropriate to your dataset. This activity explains the difference.

    • Common tasks - classification and regression

      An overview of the common machine learning tasks classification and regression, plus a look at some other frequently used terminology.

    • Software tools

      An overview to the software packages used in the course, including Scikit-Learn, Matplotlib, and Pandas.

    • Summary and review

      Summary and review of week 1, with a quiz and practical activity.

  • Week 2

    Data and features

    • Types of data and features

      An overview of the types of data used in machine learning, and an introduction to features and feature extraction.

    • Feature extraction

      A look at feature extraction for use in machine learning. A particular focus on feature extraction from image data.

    • Labelling image data

      Image data often needs to be labelled or annotated for use in machine learning models. This activity goes over why and how you might annotate image data, and introduces some software tools.

    • Pre-processing data

      How to deal with noisy and incomplete data, and a look at pre-processing of image data.

    • Summary and review

      A review of the week's content, with a practical and a quiz.

  • Week 3

    Common techniques

    • Introduction

      In week 3 we will look in more detail at some common machine learning methods for clustering, classification, and regression. Plus a look at methods for model evaluation, visualisation, and selection.

    • Clustering

      A closer look at clustering methods, in particular K-means clustering.

    • Classification

      A closer look at the common classification methods of Decision Trees and Naive Bayes.

    • Regression

      A look at regression techniques, in particular linear regression.

    • Evaluation, visualisation, and selection

      How to evaluate your machine learning models. Includes accuracy, precision, recall, and F scores. Plus a look at ways to visualise your results, including confusion matrices, and some advice on model selection.

    • Summary and review

      A review of the week's content, with a practical and a quiz.

  • Week 4

    Tips and tricks

    • Introduction

      An introduction to Week 4, Tips and Tricks.

    • Good Training Practice

      A look at choice of features, using learning performance curves to improve model training, splitting datasets and use of cross-validation.

    • Data augmentation

      A look at methods to artifically increase the size of datasets by using data augmentation.

    • Common challenges

      Including overfitting, regularisation, the "Curse of Dimensionality", and class imbalance.

    • Summary and review

      A review of the week's content, with a quiz and practical activity.

  • Week 5

    Deep learning

    • What is deep learning?

      A look at what deep learning is and how it compares with the machine learning learning methods we have considered previously in the course.

    • Neural networks and deep learning

      An overview of how deep learning systems are constructed. Starting with perceptrons, then neural networks, and finally convolutional neural networks.

    • Some simple tools

      We won't be doing any practical deep learning within this course. But to give you a taste of how we will cover it in future units, we introduce Python notebooks and Colab, and provide some links for further reading.

    • Summary and review

      A review of the week and course's content, with a quiz

When would you like to start?

Start straight away and join a global classroom of learners. If the course hasn’t started yet you’ll see the future date listed below.

  • Available now

Learning on this course

On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.

What will you achieve?

By the end of the course, you‘ll be able to...

  • Explain what machine learning is, and how it relates to image data
  • Perform simple image labelling and pre-processing tasks
  • Classify supervised, unsupervised and semi-supervised machine learning techniques
  • Code some simple machine learning scripts using Python Scikit-Learn
  • Describe some common machine learning tasks, such as clustering, regression, and classification
  • Investigate deep learning, and how it differs from machine learning

Who is the course for?

This course is designed for researchers and other professionals working in plant phenotyping or related bioscience disciplines, who want to know more about how machine learning can be used with image data.

What software or tools do you need?

Any software needed for the course is available to download for free and introduced as part of the course content.

Who will you learn with?

Andrew French is a Professor of Computer Science at the University of Nottingham. His area of research is developing novel image analysis methods, specifically for biological images.

I have been a researcher in artificial intelligence since 1989, when I started my PhD. In 2019, I joined the University of Lincoln to work on the application of AI and robotics to agriculture.

Who developed the course?

The University of Nottingham

The University of Nottingham is committed to providing a truly international education, inspiring students with world-leading research and benefitting communities all around the world.

University of Lincoln

The University of Lincoln is rated in the top 20 UK universities for student satisfaction in the Guardian University Guide 2022 and the Complete University Guide 2022.

  • Established

    1996
  • Location

    Lincoln, Lincolnshire, UK

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Choose the best way to learn for you!

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Automatically renews

Develop skills to further your career

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  • Access to 1,000+ courses
  • Learn at your own pace
  • Discuss your learning in comments
  • Digital certificate when you're eligible

Cancel for free anytime

Buy this course

$134/one-off payment

Fulfill your current learning need

  • Access to this course
  • Learn at your own pace
  • Discuss your learning in comments
  • Printed and digital certificate when you’re eligible

Limited access

Free

Sample the course materials

  • Access expires 2 Jul 2024

Find out more about certificates, Unlimited or buying a course (Upgrades)

Sale price available until 3 June 2024 at 23:59 (UTC). T&Cs apply.

Find out more about certificates, Unlimited or buying a course (Upgrades)

Sale price available until 3 June 2024 at 23:59 (UTC). T&Cs apply.

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