Part I: Decision Tree, ID3 Algorithm, Rules, Classification, Entropy, Information Gain, Supervised

This Video explains concept of probability, gain & information gain or gain.
How to construct decision tree?
How to obtain rules from Decision Tree

Data Mining Playlists

Link of Previous videos
Frequent Pattern Tree # (FP)

# Apriori Algorithm

# Frequent, Closed, Maximal Itemset

# KNN Classification

# K Medoid # PAM

# Hierarchical Algorithms, Divisive Algorithm

# K means

# KNN Clustering

# Naive Bayes Theorem

Data Mining Introduction

Types of # Attributes

# DBSCAN

Frequent Pattern Algorithm
Decision Tree
Multidimensional Association Rule
Multilevel Association Rule

[santoshpatil]

Quality video. Concept of probability and entropy explained with simple example

[sidharthchandran]

Easy and Good Explanation of the concepts and classifications.

[snehalshinde]

Thank you ma’am, helped me to clear my concept

[krantijaybhay]

Thank you mam for such nice explanation of entropy and information gain... Very good examples

[Anuragpandey-guog]

Ma'am, please make a video for entropy formula derivation also if possible.