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Applications of Machine Learning in Plant Science

Adrian Powell and Gaurav Moghe

This course provides learners with an introduction to applications of machine learning in the plant sciences. Learners will be given an introduction to machine learning including supervised learning, test validation, learning via gradient methods, neural networks, regression, and parameter optimization, with examples of how these techniques can be used in the context of plant biology. We will learn about examples from scientists currently applying machine learning in the plant sciences. A series of Python exercises in Jupyter will enable learners to apply their learning to questions in plant science. By the end of the course, learners will be able to describe key concepts in machine learning, implement machine learning approaches in the plant sciences, and evaluate these implementations. The course is asynchronous and student-paced, and it is offered as audit-only. Assessments will primarily consist of self-assessments, such as short check-your-understanding quizzes.

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What's inside

Learning objectives

  • By the end of the course, learners will be able to:
  • Describe key concepts in machine learning,
  • Identify examples of how machine learning can be applied in the plant sciences,
  • Implement machine learning approaches in the plant sciences, and evaluate these implementations.

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Explores machine learning applications in the context of plant biology, providing relevant examples
Provides hands-on Python exercises in Jupyter for learners to apply their learning to plant science
Offers a comprehensive overview of machine learning concepts, including supervised learning, gradient methods, and neural networks
Taught by experienced instructors Adrian Powell and Gaurav Moghe, who demonstrate their expertise in the field
Requires learners to have a basic understanding of machine learning concepts, which may pose a barrier to complete beginners

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Reviews summary

Ml applications in plant science

According to learners, this course offers a solid and practical introduction to the applications of machine learning in plant science. Students particularly praise the clear explanations that effectively bridge the gap between two complex fields. The hands-on Python exercises in Jupyter are consistently highlighted as a major strength, enabling practical implementation of ML approaches. While the self-paced, audit-only format suits many, some note that a foundational understanding of either ML or plant biology is beneficial to fully grasp the material.
Asynchronous and self-paced nature offers flexibility.
"The self-paced audit format was perfect for my busy schedule, allowing me to learn at my own speed."
"I enjoyed the flexibility of completing the course on my own time without strict deadlines."
"While flexible, I sometimes missed the direct instructor interaction that paid courses offer."
Course excels at connecting ML to plant biology.
"This course masterfully bridges machine learning with real-world plant science applications, which is exactly what I needed."
"I found the examples from current scientists incredibly insightful for understanding how ML is used in plant sciences."
"The instructors did a great job explaining complex ML concepts in a way that relates to biological data."
Hands-on coding invaluable for applying concepts.
"The Jupyter notebooks are fantastic; they really helped me understand how to apply the concepts directly."
"I appreciate the hands-on coding and projects; they're the strongest part of the course for me."
"Working through the Python exercises gave me the confidence to implement ML in my own research."
Some background in ML or biology aids comprehension.
"As a plant biologist, I found the ML concepts introduced very quickly. Some prior ML exposure would have helped."
"I came from a pure ML background, and while the course was good, I sometimes wished for more depth in plant biology basics."
"It's a good introduction, but I think having some foundational knowledge in either area is beneficial for true mastery."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Applications of Machine Learning in Plant Science with these activities:
Engage in Peer-to-Peer Discussions on Plant Genomics
Exchange knowledge and insights with peers, fostering collaborative learning and expanding understanding of plant genomics.
Show steps
  • Identify a peer-to-peer forum or platform relevant to plant genomics.
  • Engage in discussions, share ideas, and ask questions.
  • Collaborate on projects or research initiatives.
Practice Implementing Different Machine Learning Algorithms
Improve proficiency in implementing different machine learning algorithms, reinforcing understanding of their properties and limitations.
Browse courses on Machine Learning Algorithms
Show steps
  • Choose a specific machine learning algorithm to focus on.
  • Implement the algorithm from scratch using a programming language (e.g., Python, R).
  • Test the algorithm on various datasets and evaluate its performance.
Show all two activities

Career center

Learners who complete Applications of Machine Learning in Plant Science will develop knowledge and skills that may be useful to these careers:

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Course image
Effort
1 to 2 hours per week for 12 weeks
Level
Intermediate
Via
edX
Institution
Cornell University
Instructors
Adrian Powell
Gaurav Moghe
Language
English
Transcript
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Explores machine learning applications in the context of plant biology, providing relevant examples
Provides hands-on Python exercises in Jupyter for learners to apply their learning to plant science
Offers a comprehensive overview of machine learning concepts, including supervised learning, gradient methods, and neural networks
Taught by experienced instructors Adrian Powell and Gaurav Moghe, who demonstrate their expertise in the field
Requires learners to have a basic understanding of machine learning concepts, which may pose a barrier to complete beginners
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