-
Course
- AI
Introduction to Decision Trees
Learn the fundamentals of decision trees in machine learning. This course will teach you how decision trees work, how to implement them in Python using Scikit-learn for classification and regression problems, and how to evaluate their performance.
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.
Table of contents
About the author
Avdhesh Gaur is a Data Analyst, instructor, author, mentor, storyteller, speaker, and consultant as well when it comes to bringing insights out of data and presenting the story behind. His career spans more than 6 years with a focus on building Data-driven BI models using Tableau & numerous other statistical data analysis Techniques.
More Courses by Avdhesh