Overview
Curriculum
In this module, we will explore decision tree learning and focus on how decision trees are constructed for supervised classification tasks. We will learn to apply splitting criteria like Gini index and how to evaluate model performance. Practical exercises and quizzes at the end will help you to apply the learned skills to real datasets and benchmark your performance. By the end of this module, you will be equipped to confidently implement decision tree classifiers.
What You'll Learn
- Understand the fundamentals of decision tree learning
- Learn how to choose optimal split points using criteria like Gini index
- Explore key decision tree concepts such as depth, overfitting, and pruning
- Apply decision tree algorithms to real-world datasets through hands-on exercises
- Gain experience with model interpretation and visualizing tree-based decisions
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