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Train ML Models

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

Instructor: ansrsource instructors

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

See how employees at top companies are mastering in-demand skills

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Build your subject-matter expertise

This course is part of the Satellite Imagery, Remote Sensing & Machine Learning Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 3 modules in this course

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.

Module details

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.

What's included

2 videos2 readings2 assignments

2 videosTotal 11 minutes

Introduction and Welcome4 minutes
Extracting Spectral and Texture Features Step-by-Step7 minutes

2 readingsTotal 9 minutes

From Raw Imagery to Reliable Classification7 minutes
Walkthrough - Build a Feature Set for Land-Cover Classes2 minutes

2 assignmentsTotal 25 minutes

Hands-on Learning: Build a Feature Set for Land-Cover Classes15 minutes
Practice Quiz: Feature Engineering Check10 minutes

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.

What's included

1 video2 readings2 assignments

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