Канал: Little Dino

K-means clustering in Python - Iris dataset

K-means clustering in Python - Iris dataset

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

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

Logistic regression in Python

Logistic regression in Python

Association mining in Python

Association mining in Python

K-nearest neighbor in Python - Iris dataset

K-nearest neighbor in Python - Iris dataset

Decision tree in Python - Titanic dataset

Decision tree in Python - Titanic dataset

Introduction to Cross validation in 7 mins

Introduction to Cross validation in 7 mins

Model evaluation 4 - Model selection criteria

Model evaluation 4 - Model selection criteria

Model evaluation 3.3 - AUC (Area under the curve)

Model evaluation 3.3 - AUC (Area under the curve)

Model evaluation 3.2 - ROC curve (Derive and Interpret)

Model evaluation 3.2 - ROC curve (Derive and Interpret)

Model evaluation 3.1 - ROC curve (Why use it)

Model evaluation 3.1 - ROC curve (Why use it)

Model evaluation 2.7 - 0.632 Bootstrap

Model evaluation 2.7 - 0.632 Bootstrap

Model evaluation 2.6 - Hold-out validation

Model evaluation 2.6 - Hold-out validation

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

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

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

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

Model evaluation 2.3 - Stratified K-fold cross-validation

Model evaluation 2.3 - Stratified K-fold cross-validation

Model evaluation 2.2 - K-fold cross-validation

Model evaluation 2.2 - K-fold cross-validation

Model evaluation 2.1 - Training, Validation, Testing set

Model evaluation 2.1 - Training, Validation, Testing set

Model evaluation 1.6 - F beta score

Model evaluation 1.6 - F beta score

Model evaluation 1.5 - F1(F)-score

Model evaluation 1.5 - F1(F)-score

Model evaluation 1.4 - Recall, Precision

Model evaluation 1.4 - Recall, Precision

Model evaluation 1.3 - Sensitivity, Specificity

Model evaluation 1.3 - Sensitivity, Specificity

Model evaluation 1.2 - Class imbalance problem

Model evaluation 1.2 - Class imbalance problem

Model evaluation 1.1 - Accuracy, Error rate

Model evaluation 1.1 - Accuracy, Error rate

Naïve Bayes 3 - Laplacian correction

Naïve Bayes 3 - Laplacian correction

Naïve Bayes 2.2 - Example

Naïve Bayes 2.2 - Example

Naïve Bayes 2.1 - Introduction

Naïve Bayes 2.1 - Introduction

Naïve Bayes 1 - Probability theory

Naïve Bayes 1 - Probability theory

Gini index 1.2 - Example

Gini index 1.2 - Example

Gini index 1.1 - Introduction

Gini index 1.1 - Introduction

Introduction to Gain ratio in 5 mins

Introduction to Gain ratio in 5 mins

Information gain 2 - Example

Information gain 2 - Example

Information gain 1 - Entropy

Information gain 1 - Entropy

Introduction to Decision tree in 6 mins

Introduction to Decision tree in 6 mins

Classification - Purpose & Process

Classification - Purpose & Process

Frequent itemset mining 2 - Generating strong rules

Frequent itemset mining 2 - Generating strong rules

Frequent itemset mining 1 - Apriori algorithm

Frequent itemset mining 1 - Apriori algorithm

Association mining 5 - Null invariance

Association mining 5 - Null invariance

Association mining 4 - Chi-square statistics

Association mining 4 - Chi-square statistics

Association mining 3 - Lift

Association mining 3 - Lift

Association mining 2 - Association rules, Confidence

Association mining 2 - Association rules, Confidence

Association mining 1 - Introduction, Support

Association mining 1 - Introduction, Support

Basic OLAP operations in 5 mins

Basic OLAP operations in 5 mins

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

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

Introduction to Data warehouse

Introduction to Data warehouse

Proximity measures 5 - Cosine similarity

Proximity measures 5 - Cosine similarity

Proximity measures 4 - Ordinal data

Proximity measures 4 - Ordinal data

Proximity measures 3.2 - Numeric data (Minkowski distance)

Proximity measures 3.2 - Numeric data (Minkowski distance)

Proximity measures 3.1 - Numeric data (Normalization)

Proximity measures 3.1 - Numeric data (Normalization)

Proximity measures 2 - Binary data

Proximity measures 2 - Binary data

Proximity measures 1 - Dissimilarity matrix, Nominal data

Proximity measures 1 - Dissimilarity matrix, Nominal data

Data dispersion in statistics 2 - Variance, Standard deviation

Data dispersion in statistics 2 - Variance, Standard deviation

Data dispersion in statistics 1 - Range, IQR, Boxplot

Data dispersion in statistics 1 - Range, IQR, Boxplot

Central tendency - Mean, Median, Mode, and Skewness

Central tendency - Mean, Median, Mode, and Skewness

Data type in statistics in 7 mins

Data type in statistics in 7 mins

Introduction to Data mining (KDD) in 8 mins

Introduction to Data mining (KDD) in 8 mins