Decision Tree and Random Forest Classification using Julia

This guided project delves into glass classification using decision tree and random forest classification in Julia. It is tailored for beginners unfamiliar with these concepts, providing clear explanations and hands-on learning opportunities. Throughout the course, you will have access to a cloud desktop with pre-installed software, enabling you to code alongside the instructor. This practical approach fosters active learning and ensures a comprehensive understanding of the subject matter.

• Learn about stumps, decision trees, and random forests.
• Explore how to evaluate the performance of a decision tree and random forest.
• Work with a real-world dataset, gaining practical experience in glass classification.

Special features of this course include simple explanations of important concepts, use of images for enhanced understanding, and challenges to reinforce learning. Note that this project is best suited for learners in the North America region, with plans to expand the experience to other regions in the future.