13:11

K-means clustering in Python - Iris dataset

12:32

K-fold cross validation (hyper-parameter tuning) in Python

15:14

Logistic regression in Python

10:40

Association mining in Python

10:42

K-nearest neighbor in Python - Iris dataset

09:06

Decision tree in Python - Titanic dataset

06:56

Introduction to Cross validation in 7 mins

02:56

Model evaluation 4 - Model selection criteria

03:57

Model evaluation 3.3 - AUC (Area under the curve)

07:59

Model evaluation 3.2 - ROC curve (Derive and Interpret)

05:53

Model evaluation 3.1 - ROC curve (Why use it)

06:06

Model evaluation 2.7 - 0.632 Bootstrap

02:51

Model evaluation 2.6 - Hold-out validation

05:49

Model evaluation 2.5 - Leave-1-out cross-validation

04:34

Model evaluation 2.4 - Leave-p-out cross-validation

03:21

Model evaluation 2.3 - Stratified K-fold cross-validation

05:29

Model evaluation 2.2 - K-fold cross-validation

07:40

Model evaluation 2.1 - Training, Validation, Testing set

05:40

Model evaluation 1.6 - F beta score

06:35

Model evaluation 1.5 - F1(F)-score

05:47

Model evaluation 1.4 - Recall, Precision

06:22

Model evaluation 1.3 - Sensitivity, Specificity

05:56

Model evaluation 1.2 - Class imbalance problem

05:57

Model evaluation 1.1 - Accuracy, Error rate

03:18

Naïve Bayes 3 - Laplacian correction

04:52

Naïve Bayes 2.2 - Example

05:40

Naïve Bayes 2.1 - Introduction

04:51

Naïve Bayes 1 - Probability theory

04:18

Gini index 1.2 - Example

05:30

Gini index 1.1 - Introduction

05:06

Introduction to Gain ratio in 5 mins

06:27

Information gain 2 - Example

05:04

Information gain 1 - Entropy

05:43

Introduction to Decision tree in 6 mins

07:09

Classification - Purpose & Process

05:51

Frequent itemset mining 2 - Generating strong rules

06:41

Frequent itemset mining 1 - Apriori algorithm

04:23

Association mining 5 - Null invariance

07:12

Association mining 4 - Chi-square statistics

06:30

Association mining 3 - Lift

05:41

Association mining 2 - Association rules, Confidence

05:53

Association mining 1 - Introduction, Support

04:59

Basic OLAP operations in 5 mins

05:35

Modeling cubes in RB - Star schema, Snowflake schema, Fact constellation

06:13

Introduction to Data warehouse

04:29

Proximity measures 5 - Cosine similarity

04:45

Proximity measures 4 - Ordinal data

07:09

Proximity measures 3.2 - Numeric data (Minkowski distance)

06:24

Proximity measures 3.1 - Numeric data (Normalization)

06:34

Proximity measures 2 - Binary data

05:34

Proximity measures 1 - Dissimilarity matrix, Nominal data

06:14

Data dispersion in statistics 2 - Variance, Standard deviation

06:23

Data dispersion in statistics 1 - Range, IQR, Boxplot

07:11

Central tendency - Mean, Median, Mode, and Skewness

06:59

Data type in statistics in 7 mins

08:06

Introduction to Data mining (KDD) in 8 mins