This course delves into tree-based models, starting with the fundamentals of building and interpreting a Decision Tree for classification and regression tasks. You will learn critical concepts like variance reduction and how to determine feature importance to understand what drives your model's predictions. We then explore the Random Forest algorithm in detail, breaking down how it combines many trees through bagging to create a more robust and accurate ensemble model. The course provides a clear comparison of the trade-offs between a single Decision Tree's simplicity and a Random Forest's superior performance and resistance to overfitting. All concepts are grounded in practical telecom use cases, such as predicting customer churn or diagnosing network faults. You will finish with the skills to select, implement, and optimize the right tree-based model for your specific data challenge.