Submission information
Submission Number: 356
Submission ID: 5302
Submission UUID: a7a5c06a-9d86-46fe-8074-d14e702ea200
Submission URI: /form/resource
Created: Wed, 05/07/2025 - 09:50
Completed: Wed, 05/07/2025 - 10:38
Changed: Thu, 08/07/2025 - 10:02
Remote IP address: 136.56.97.91
Submitted by: Nicole Corbin
Language: English
Is draft: No
Webform: Knowledge Base Resources
| Approved | Yes |
|---|---|
| Title | Supervised Machine Learning Readiness |
| Category | Learning |
| Skill Level | Beginner |
| Description | Supervised Machine Learning Readiness is a self-paced, beginner-friendly program designed for Earth systems scientists to explore the core principles of supervised machine learning. This series uses a combination of step-by-step frameworks, exploratory widgets, and low-code exercises in Jupyter Notebooks, to explore the full cycle of machine learning model development. No programming experience is required. By the end of the series, you will be able to recognize when machine learning is an appropriate tool and critically evaluate machine learning in Earth systems science contexts. Access requires a free NSF Unidata eLearning account. |
| Link to Resource | |
| Tags | supervised-learning, machine-learning, atmospheric-physics, earth-sciences, workforce-development, jupyterhub |
| Domain | At-Large |
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