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Classification with Decision Trees

Great Learning via YouTube

Overview

This course on Classification using Decision Trees aims to teach learners how to implement classification with decision trees using the R language. By the end of the course, students will be able to understand the concept of classification, decision tree learning, advantages of tree-based models, and various key functions and concepts related to decision trees. The course covers topics such as the Gini index, entropy, misclassification error, measuring impurity, and different types of decision tree algorithms. The teaching method includes theoretical explanations, case studies, and practical demonstrations. This course is intended for individuals interested in machine learning, specifically in the area of classification using decision trees.

Syllabus

➤ Skip Intro: .
Introduction.
What is Classification?.
What is Decision Tree Learning?.
Advantages of using Tree-based Models.
Gold Loan Case Study.
Decision Tree in-depth Concept.
Gini Index, Entropy, Misclassification Error.
Measuring Impurity.
Types of Decision Tree algorithms.

Taught by

Great Learning

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