What you'll learn

Decision trees are one of the most intuitive and powerful tools in machine learning, yet understanding how they work and applying them effectively can be challenging. In this course, Introduction to Decision Trees, you’ll gain the ability to build and interpret decision tree models for classification and regression tasks. First, you’ll explore the basic structure and components of decision trees, understanding how they make predictions using recursive partitioning. Next, you’ll discover key tree-splitting criteria such as information gain and Gini impurity, learning how these impact decision-making in models. Finally, you’ll learn how to implement decision tree models using Scikit-learn in Python, visualize their structure, and evaluate performance using metrics like accuracy and RMSE. When you’re finished with this course, you’ll have the skills and knowledge of decision trees needed to confidently apply them in real-world machine learning projects.