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.
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.
- 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.
Good to know
Save this course
Save
Activities
Engage in Peer-to-Peer Discussions on Plant Genomics
Show steps
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
Show steps
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
Engage in Peer-to-Peer Discussions on Plant Genomics
Show steps
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
Show steps
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.
Career center
Reading list
Share
Similar courses
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
