Earners of this badge have completed a four-part, hands-on machine learning module using real Chesapeake Bay Program water quality monitoring data. Working in Google Colab with LLM-assisted Python coding, participants built a complete ML workflow — from raw data cleaning through model training, interpretation, and cross-station prediction. Participants trained both a Random Forest and a Multilayer Perceptron (MLP) neural network to predict bottom dissolved oxygen from surface water quality variables, evaluated model performance using RMSE and R-squared, interpreted model behavior using SHAP values, and applied saved models to an independent monitoring station. The module demonstrates how modern ML tools and AI-assisted coding can accelerate environmental data science research.