G. Decision Trees

The Decision Trees Design course is designed to help learners understand and apply the theory of Decision Trees in real-world applications. The content covers the fundamentals of Decision Trees, detailing how each component of the decision tree works, along with analysis and evaluation of the outcomes produced by this model.

Participants will learn about key concepts such as Entropy, Information Gain, and Pruning Techniques, which are essential for creating effective Decision Trees. Additionally, the course includes applications of Decision Trees in both Classification and Regression tasks, along with real-world examples presented through Case Studies to ensure learners can confidently apply their knowledge.

The course also includes demonstrations of how to build a Decision Tree model, from data import to tuning and performance testing. Learners will have opportunities for hands-on practice through quizzes and live tests to assess their understanding and ability to use Decision Trees to solve real problems. By the end of the course, participants will be well-prepared to implement Decision Trees effectively in their projects.