This learning series is designed to guide you through the very basics of supervised machine learning in the Earth Systems Sciences. You will discover how machine learning is used by scientists, the process for supervised machine learning model development, how data plays a crucial role in making good predictions, and how to be an effective and ethical user of machine learning tools. You will also learn that machine learning is not a catch-all solution to every problem!
You won't be expected to have any programming skills to complete this learning series. Through simple schematics and widgets, you will be guided through the conceptual process for developing and using supervised machine learning for science. Expect to finish the series with the skills to assess the suitability of supervised machine learning for new problems and question supervised machine learning analyses you encounter.
Raw data and original size media available upon request.
To request a JupyterHub for using these modules in your classroom, contact the NSF Unidata Science Gateway program.
This work was supported by NSF Unidata under award #2319979 from the US National Science Foundation. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.