courseraАнализ данных

Train ML Models

Курс от Coursera

Начальный≈ 3 чАнглийский

О курсе

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.

Навыки, которые вы освоите

Random Forest Algorithm Model Evaluation Feature Engineering Environmental Engineering Supervised Learning Verification And Validation Model Training Data Science Image Analysis Geospatial Mapping Predictive Modeling Applied Machine Learning

Программа курса

3 модулей · 20 учебных материалов

01From Pixels to Predictors7 материалов

Introduction and WelcomeВидео Why Feature Engineering Matters in Land-Cover ClassificationDIALOGUE From Raw Imagery to Reliable ClassificationЧтение Extracting Spectral and Texture Features Step-by-StepВидео

Учитесь у экспертов

Professionals in the Industry

ansrsource instructors