Supervised Machine Learning Readiness
Overview
This learning series uses a combination of step-by-step frameworks, exploratory widgets, and low-code exercises in Jupyter Notebooks to explore the full cycle of supervised machine learning model development.
This work was supported by the US National Science Foundation under CyberTraining award #2319979.
Program goal
Bridge the gap between machine learning concepts and low-code, real-world applications in the Earth Systems Sciences.
My responsibilities
Tools
Machine Learning Foundations
No-code conceptual introduction to supervised machine learning in the Earth Systems Sciences. Learners explore problem framing, data handling, and model development concepts with support from no-code widgets.
Machine Learning Applications
Low-code exploration of concepts in Module 01. Learners create and test hypotheses using Jupyter widgets and pre-populated code. Learners work with real-world data and test the effect of various preprocessing and training strategies.
Watch the introduction video below.
Machine Learning Analysis
Team-based lab module. Groups of students select a scenario to create and execute a machine learning strategy for. Groups must justify the choice of data, model, and other decisions made for their scenario.
