Train ML Models

This course equips learners with practical, job-ready skills to train and evaluate supervised machine learning models for land-cover classification. Learners progress through an end-to-end analytical workflow, beginning with spectral and texture feature engineering, followed by training a Random Forest classifier, and concluding with rigorous validation using confusion-matrix-based accuracy assessment. By the end of the course, learners produce a land-cover map that meets a minimum accuracy threshold, mirroring real-world data analysis workflows.