Learning Series
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.
This work was supported by NSF Unidata under award #2319979 from the US National Science Foundation.
Microlearning
In this module, learners will be introduced to the THREDDS Data Server and Siphon data access architecture and practice making a request for remote data in Python.
This module is recommended for new users of Siphon and the THREDDS Data Server.
Microlearning
In this module, learners will review the multidimensional nature of Earth Systems Science data, explore the hierarchical nature of multidimensional data structures, and search for critical metadata within these structures.
This module is recommended for new users of netCDF data.