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.

Train ML Models
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Train ML Models
This course is part of Satellite Imagery, Remote Sensing & Machine Learning Specialization

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April 2026
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