Build practical machine learning skills by implementing and evaluating Random Forest models in Python. In this hands-on course, you'll work through a complete supervised learning workflow using the SONAR dataset, from data preparation and exploration to decision tree construction and Random Forest model evaluation.

Through guided coding exercises, you'll learn how to load and inspect data, apply decision tree splitting techniques using the Gini index, and evaluate classification performance with cross-validation. You'll then assemble a Random Forest classifier and assess its effectiveness using structured validation approaches and performance analysis. This course is designed for learners with a basic understanding of Python who want to strengthen their knowledge of supervised machine learning through practical, code-based learning. Rather than focusing only on theory, you'll implement each step of the modeling process and evaluate model performance using established validation techniques. By the end of the course, you'll be able to build and evaluate Random Forest classifiers in Python, apply data preparation techniques, use impurity measures to construct decision trees, and assess classification models with confidence. If you're looking for a practical introduction to Random Forests and supervised learning, this course provides a structured, project-based learning experience.