The University of Nottingham
- Start Date: 11 Sep, 2023
- 5 weeks
- Study Content: Videos
Master the principles and applications of machine learning for image data to harness its potential for plant phenotyping.
Course Fee: Free
Certificate Cost: See Fees and Eligibility
Course Description
This course is made available through the eLearnAfrica and FutureLearn partnership.
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.
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.
Any software needed for the course is available to download for free and introduced as part of the course content.
Certificate cost may vary. You will be redirected to the host page for cost and payment options.
The University of Nottingham
The University of Nottingham is committed to providing a truly international education, inspiring students, producing world-leading research and benefiting the communities around campuses in the UK, China and Malaysia. The purpose of the University is to improve life for individuals and societies worldwide. By bold innovation and excellence in all that it does, the University makes both knowledge and discoveries matter.
The University of Nottingham has 42,000 students at award-winning campuses in the United Kingdom, China and Malaysia. It was ‘one of the first to embrace a truly international approach to higher education’, according to the Sunday Times University Guide 2013. It is also one of the most popular universities among graduate employers, one of the world’s greenest universities, and winner of the Times Higher Education Award for ‘Outstanding Contribution to Sustainable Development’. It is ranked in the UK’s Top 10 and the World’s Top 75 universities by the Shanghai Jiao Tong and the QS World Rankings.
More than 90 per cent of research at The University of Nottingham is of international quality, according to the most recent Research Assessment Exercise. The University aims to be recognised around the world for its signature contributions, especially in global food security, energy & sustainability, and health. It won a Queen’s Anniversary Prize for Higher and Further Education for its research into global food security.
This institution is available on eLearnAfrica through partnership with FutureLearn.
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Effective Date: September 22, 2016