Decision Tree and Random Forest Modeling in Python

Tree-based machine learning algorithms offer an intuitive, powerful way to solve complex classification and regression problems. This text-based course guides you through the principles and implementation of decision trees and random forests, giving you the practical coding skills needed to train accurate, interpretable models in Python. You will begin with essential machine learning definitions and structural concepts before exploring how individual decision trees split data and generate predictions. Through clear written explanations and practical code examples, you will progress to building robust ensemble models and tuning hyperparameters for optimal predictive performance. What you'll learn: - Understand core terminology, node splitting criteria, and foundational concepts behind tree-based algorithms - Build classification and regression decision trees using Python and scikit-learn - Apply pruning techniques and depth constraints to prevent model overfitting - Construct random forest ensembles to reduce variance and boost prediction stability - Evaluate model performance using modern cross-validation methods and metrics - Analyze feature importance to extract meaningful insights from trained models The course begins with foundational vocabulary and mathematical logic before walking step by step through implementation, hyperparameter optimization, and real-world tabular data modeling. This course is designed for aspiring data analysts, developers, and machine learning beginners with basic Python familiarity. Start reading today to master tree-based modeling in Python.