This hands-on course equips learners with the skills to implement, analyze, and evaluate the Random Forest algorithm using Python. Designed around a real-world classification problem using the SONAR dataset, the course guides learners through the entire pipeline—from data loading and preprocessing to constructing decision trees and assembling Random Forest models.
Python: Implement & Evaluate Random Forests for ML
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Recently updated!
September 2025
Assessments
4 assignments
Taught in English
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There is 1 module in this course
This module introduces learners to the foundational concepts required to implement and evaluate a Random Forest algorithm using Python. Through practical coding exercises and structured exploration of the SONAR dataset, learners will understand how to prepare data, construct decision trees, and assess classification performance using key metrics and validation techniques. The module culminates in assembling a Random Forest model and analyzing its effectiveness in real-world scenarios.
What's included
13 videos4 assignments
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