Little Dino - VideoAndMovie

Hey guys, I hope these short videos about statistics, data mining, and programming make your life a little bit easier :-)

K-means clustering in Python - Iris dataset

13:11

K-means clustering in Python - Iris dataset

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

12:32

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

Logistic regression in Python

15:14

Logistic regression in Python

Association mining in Python

10:40

Association mining in Python

K-nearest neighbor in Python - Iris dataset

10:42

K-nearest neighbor in Python - Iris dataset

Decision tree in Python - Titanic dataset

9:06

Decision tree in Python - Titanic dataset

Introduction to Cross validation in 7 mins

6:56

Introduction to Cross validation in 7 mins

Model evaluation 4 - Model selection criteria

2:56

Model evaluation 4 - Model selection criteria

Model evaluation 3.3 - AUC (Area under the curve)

3:57

Model evaluation 3.3 - AUC (Area under the curve)

Model evaluation 3.2 - ROC curve (Derive and Interpret)

7:59

Model evaluation 3.2 - ROC curve (Derive and Interpret)

Model evaluation 3.1 - ROC curve (Why use it)

5:53

Model evaluation 3.1 - ROC curve (Why use it)

Model evaluation 2.7 - 0.632 Bootstrap

6:06

Model evaluation 2.7 - 0.632 Bootstrap

Model evaluation 2.6 - Hold-out validation

2:51

Model evaluation 2.6 - Hold-out validation

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

5:49

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

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

4:34

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

Model evaluation 2.3 - Stratified K-fold cross-validation

3:21

Model evaluation 2.3 - Stratified K-fold cross-validation

Model evaluation 2.2 - K-fold cross-validation

5:29

Model evaluation 2.2 - K-fold cross-validation

Model evaluation 2.1 - Training, Validation, Testing set

7:40

Model evaluation 2.1 - Training, Validation, Testing set

Model evaluation 1.6 - F beta score

5:40

Model evaluation 1.6 - F beta score

Model evaluation 1.5 - F1(F)-score

6:35

Model evaluation 1.5 - F1(F)-score

Model evaluation 1.4 - Recall, Precision

5:47

Model evaluation 1.4 - Recall, Precision

Model evaluation 1.3 - Sensitivity, Specificity

6:22

Model evaluation 1.3 - Sensitivity, Specificity

Model evaluation 1.2 - Class imbalance problem

5:56

Model evaluation 1.2 - Class imbalance problem

Model evaluation 1.1 - Accuracy, Error rate

5:57

Model evaluation 1.1 - Accuracy, Error rate

Naïve Bayes 3 - Laplacian correction

3:18

Naïve Bayes 3 - Laplacian correction

Naïve Bayes 2.2 - Example

4:52

Naïve Bayes 2.2 - Example

Naïve Bayes 2.1 - Introduction

5:40

Naïve Bayes 2.1 - Introduction

Naïve Bayes 1 - Probability theory

4:51

Naïve Bayes 1 - Probability theory

Gini index 1.2 - Example

4:18

Gini index 1.2 - Example

Gini index 1.1 - Introduction

5:30

Gini index 1.1 - Introduction