Machine Learning Applications in the Earth Systems Sciences
In this Jupyter Notebook, you are tasked with planning, implementing, and evaluating a machine learning solution for a real-world scenario. Given pre-configured code blocks and prepared data, you will create a problem statement, explore the data, experiment with model development, and ultimately make a recommendation on the utility of machine learning for your scenario.
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Details
Learning Series
Machine Learning in the Earth Systems Sciences
Intended Audience
- Earth Systems scientists
- New and aspiring machine learning users
- Graduate and upper-level undergraduate students
Learning Objectives
- Describe the data used in the scenario, including discussion of the context to the physical world
- Create and make strategic refinements to a machine learning model following a guided low-code workflow
- Describe a multifaceted classification model evaluation, including accuracy, precision, and recall scores
- Justify a decision on utility of a machine learning model based on evaluation metrics, physical context, impacts on the Earth Systems Science scenario
Suggested Prerequisite Skills
- Essential Digital Competencies
- Completion of Machine Learning Foundations in the Earth Systems Sciences
Author
NSF Unidata
In partnership with Metropolitan State University of Denver
This work was supported by NSF Unidata under award #2319979 from the US National Science Foundation.
Format
Jupyter Notebook
Duration
1 hour
Technical Requirements
For classroom support, you may request a JupyterHub from NSF Unidata.
Last Update
March 2025