Train ML Models

Overview

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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.

Syllabus

From Pixels to Predictors

You will explore why raw imagery alone is insufficient for supervised classification and how engineered features improve model performance. The lesson focuses on practical extraction of spectral bands and texture metrics used in land-cover analysis.

Training a Random Forest Classifier on Imagery Data

You will apply engineered features to train a Random Forest classifier. Emphasis is placed on intuition: how trees vote, how parameters affect performance, and how to avoid beginner mistakes.

Evaluating Accuracy: Confusion Matrices & Model Validation

You will evaluate whether the model meets job requirements by interpreting confusion matrices and accuracy metrics. The lesson emphasizes decision-making, not just calculation.