Decision Tree – Theory, Application and Modeling using R

Course Curriculum

Chapter 1: Introduction to decision tree

Lecture 1: Welcome Note

Lecture 2: Section Overview

Lecture 3: Need of a decision tree

Lecture 4: Anatomy of a Decision Tree

Lecture 5: Gain From a Decision Tree

Lecture 6: KS of a decision tree

Lecture 7: Business Application of a Decison tree

Lecture 8: Defintions related with Objective segmentation

Lecture 9: Decision Tree vs Logistic Regression

Lecture 10: FAQ: for Introduction section

Lecture 11: Section PDF

Chapter 2: 1 A : Model Design – Ensure actionable data for modeling

Lecture 1: Section Overview

Lecture 2: Model Design in Principal

Lecture 3: Model Design Precautions

Lecture 4: Model Design Outcome

Lecture 5: Performance Window Design

Lecture 6: Data Audit n Treatment Guideline and section PDF

Chapter 3: Demo of Decision Tree development using R

Lecture 1: Section Overview

Lecture 2: Understand The data for Demo

Lecture 3: View resource to download files

Lecture 4: How to download excel files, R program etc?

Lecture 5: Install R and R Studio

Lecture 6: First Decision Tree in R

Lecture 7: Second Decision Tree in R

Lecture 8: About New additions in the course work

Lecture 9: Practical_Usage_of_classification_tree – demo

Lecture 10: Practical_Usage_of_classification_tree – assignment

Lecture 11: Practical_Usage_of_classification_tree – assignment solution

Lecture 12: Section PDF

Chapter 4: Algorithm behind decision tree

Lecture 1: Section Overview

Lecture 2: Intutive Understanding of Numeric Variable Split

Lecture 3: GINI Index of a node

Lecture 4: GINI Index of a Split

Lecture 5: CART in action : Decide which variable n its value for the split

Lecture 6: Practical approach of Decision Tree Development

Lecture 7: Some practical situation of decision tree model validation

Lecture 8: Implementing decision tree model

Lecture 9: Auto Pruning Technique of decision tree development part 1

Lecture 10: K Fold Cross Validation

Lecture 11: Auto Pruning Using R.

Lecture 12: Developing Regression Tree Using R

Lecture 13: Interpret Regression Tree Output

Lecture 14: Another Regression Tree Using R

Lecture 15: Practical_Usage_of_Regression_tree – demo part 1

Lecture 16: Practical_Usage_of_Regression_tree – demo part 2

Lecture 17: Practical_Usage_of_Regression_tree – assignment

Lecture 18: Practical_Usage_of_Regression_tree – assignment solution

Lecture 19: Linear Regression vs Regression tree

Lecture 20: CHAID Algorithm

Lecture 21: CHAID vs CART

Lecture 22: FAQ – for algorithm behind decision tree section

Lecture 23: Section PDF

Lecture 24: Appendix Content – Chi Square Statistic

Lecture 25: Appendix Content – Feel The Chi Square Statistic

Lecture 26: Appendix content – Degree of freedom of a cross tab

Lecture 27: Appendix content – Chi Square Distribution

Lecture 28: Appendix content – PDF

Chapter 5: Other algorithm of decision tree development

Lecture 1: Section Overview

Lecture 2: Entropy of a Node

Lecture 3: Entropy of a Split

Lecture 4: ID3 Method

Lecture 5: Random Forest Method

Lecture 6: R syntax for Random Forest

Lecture 7: Ensemble Learning – Bagging and Bossting

Lecture 8: Section FAQ – Other algorithm of decision tree

Lecture 9: Introduction to Gradient Boosting

Lecture 10: FAQ – For other algorithm of decision tree

Lecture 11: Section PDF

Lecture 12: Bonus topic – Decision Tree using Python

Lecture 13: Bonus Topic – Analytics / Data Science / Machine Learning Interview questions

Lecture 14: Closure Note