Python
How to Install Python
How to Edit User’s Preferences and Settings
How to change text color and background color
Data Handling
How to Import Libraries
How to Know and Change the Working Directory
How to Import CSV Data
How to Set Dependent Variables and Independent Variables (iloc example)
How the Handle Missing Data with Imputer
How to Set Categorical Data (Dummy Variable)
How to Split Data into Training Set and Testing Set
How to Apply Feature Scaling
Regression
Simple Linear Regression
1.) Import Libraries and Import Dataset
2.) Split the Training Set and Testing Set
3.) Training the Model
4.) Predicting Results
5.) Visualize Results
Multiple Linear Regression
1.) Import Libraries and Import Dataset
2.) LabelEncoder OneHotEncoder
3.) Split the Training Set and Testing Set
4.) Training the Model
5.) Predicting Results
6.) Backward Elimination
Polynomial Regression
1.) Import Libraries and Import Dataset
2.) Split the Training Set and Testing Set
3.) Training the Model
4.) Predicting Results
5.) Visualize Results
Support Vector Regression (SVR)
1.) Import Libraries and Import Dataset
2.) Split the Training Set and Testing Set
3.) Feature Scaling
4.) Training the Model
5.) Predicting Results
6.) Visualize Results
Decision Tree Regression
1.) Import Libraries and Import Dataset
2.) Split Dataset into Training Set and Testing Set
3.) Training the Model
4.) Predicting Results
5.) Visualize Results
Random Forest Regression
1.) Import Libraries and Import Dataset
2.) Split the Training Set and Testing Set
3.) Training the Model
4.) Predicting Results
5.) Visualize Results
Logistic Regression
1.) Import Libraries and Import Dataset
2.) Split the Training Set and Testing Set
3.) Feature Scaling
4.) Training the Model
5.) Predicting Results
6.) Confusion Matrix
7.) Visualize Results
Multivariate Analysis
Principal Component Analysis (PCA)
1.) Import Libraries and Import Data
2.) Split Data into Training Set and Testing Set
3.) Feature Scaling
4.) Implement of PCA
5.) Training Regression Model with PCA
6.) Predict Results with PCA Model
7.) 3×3 Confusion Matrix
8.) Visualize the Results of PCA Model
Linear Discriminant Analysis (LDA)
1.) Import Libraries and Import Data
2.) Split the Data into Training Set and Testing Set
3.) Feature Scaling
4.) Implement of LDA
5.) Training the Regression Model with LDA
6.) Predict the Result with LDA Model
7.) 3×3 Confusion Matrix
8.) Visualize the Results of LDA Model
Classification
K-Nearest Neighbors (K-NN)
Support Vector Machine (SVM)
Kernal SVM
Naive Bayes
Decision Tree Classification
Random Forest Classification
K-Mean Clustering
Hierarchical Clustering
Association Rule Learning
Apriori
1.) Import Libraries and Import Data
2.) Train Apriori Model
3.) Visualize Apriori Results
Eclat
Simple Artificial Intelligent
Multi Armed Bandit Problem
Upper Confidence Bound (UCB)
Thompson Sampling
Deep Learning
Natural Language Processing (NLP)
Artificial Neural Networks (ANN)
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN)
Self-Organizing Maps (SOM)
Boltzmann Machines
Autoencoders
XGBoost
R
How to install R
How to Install R Studio on PC
How to Install R Studio on Mac
Data Handling in R Studio
How to Import Libraries to R Studio
How to Import CSV Data in R studio
Regression in R Studio
Simple Linear Regression in R Studio
Multiple Linear Regression in R studio
Polynomial Regression in R Studio
Support Vector Regression (SVR) in R Studio
Decision Tree Regression in R studio
Random Forest Regression in R Studio
Logistic Regression in R Studio
Multivariate Analysis in R Studio
Principal Component Analysis (PCA) in R Studio
Linear Discriminant Analysis (LDA) in R Studio
Classification in R Studio
K-Nearest Neighbors (K-NN) in R Studio
Support Vector Machine (SVM) in R Studio
Kernal SVM in R Studio
Naive Bayes in R studio
Decision Tree Classification in R studio
Random Forest Classification in R Studio
K-Mean Clustering in R Studio
Hierarchical Clustering in R Studio
Association Rule Learning in R Studio
Apriori in R studio
Eclat in R Studio
Simple Artificial Intelligent in R Studio
Upper Confidence Bound (UCB) in R Studio
Thompson Sampling in R Studio
Deep Learning in R Studio
Natural Language Processing (NLP) in R Studio
Artificial Neural Networks (ANN) in R Studio
Convolutional Neural Networks (CNN) in R Studio
Recurrent Neural Networks (RNN) in R Studio
Self-Organizing Maps (SOM) in R Studio
Boltzmann Machines in R Studio
Autoencoders in R Studio
XGBoost in R Studio
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Contact
Python
How to Install Python
How to Edit User’s Preferences and Settings
How to change text color and background color
Data Handling
How to Import Libraries
How to Know and Change the Working Directory
How to Import CSV Data
How to Set Dependent Variables and Independent Variables (iloc example)
How the Handle Missing Data with Imputer
How to Set Categorical Data (Dummy Variable)
How to Split Data into Training Set and Testing Set
How to Apply Feature Scaling
Regression
Simple Linear Regression
1.) Import Libraries and Import Dataset
2.) Split the Training Set and Testing Set
3.) Training the Model
4.) Predicting Results
5.) Visualize Results
Multiple Linear Regression
1.) Import Libraries and Import Dataset
2.) LabelEncoder OneHotEncoder
3.) Split the Training Set and Testing Set
4.) Training the Model
5.) Predicting Results
6.) Backward Elimination
Polynomial Regression
1.) Import Libraries and Import Dataset
2.) Split the Training Set and Testing Set
3.) Training the Model
4.) Predicting Results
5.) Visualize Results
Support Vector Regression (SVR)
1.) Import Libraries and Import Dataset
2.) Split the Training Set and Testing Set
3.) Feature Scaling
4.) Training the Model
5.) Predicting Results
6.) Visualize Results
Decision Tree Regression
1.) Import Libraries and Import Dataset
2.) Split Dataset into Training Set and Testing Set
3.) Training the Model
4.) Predicting Results
5.) Visualize Results
Random Forest Regression
1.) Import Libraries and Import Dataset
2.) Split the Training Set and Testing Set
3.) Training the Model
4.) Predicting Results
5.) Visualize Results
Logistic Regression
1.) Import Libraries and Import Dataset
2.) Split the Training Set and Testing Set
3.) Feature Scaling
4.) Training the Model
5.) Predicting Results
6.) Confusion Matrix
7.) Visualize Results
Multivariate Analysis
Principal Component Analysis (PCA)
1.) Import Libraries and Import Data
2.) Split Data into Training Set and Testing Set
3.) Feature Scaling
4.) Implement of PCA
5.) Training Regression Model with PCA
6.) Predict Results with PCA Model
7.) 3×3 Confusion Matrix
8.) Visualize the Results of PCA Model
Linear Discriminant Analysis (LDA)
1.) Import Libraries and Import Data
2.) Split the Data into Training Set and Testing Set
3.) Feature Scaling
4.) Implement of LDA
5.) Training the Regression Model with LDA
6.) Predict the Result with LDA Model
7.) 3×3 Confusion Matrix
8.) Visualize the Results of LDA Model
Classification
K-Nearest Neighbors (K-NN)
Support Vector Machine (SVM)
Kernal SVM
Naive Bayes
Decision Tree Classification
Random Forest Classification
K-Mean Clustering
Hierarchical Clustering
Association Rule Learning
Apriori
1.) Import Libraries and Import Data
2.) Train Apriori Model
3.) Visualize Apriori Results
Eclat
Simple Artificial Intelligent
Multi Armed Bandit Problem
Upper Confidence Bound (UCB)
Thompson Sampling
Deep Learning
Natural Language Processing (NLP)
Artificial Neural Networks (ANN)
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN)
Self-Organizing Maps (SOM)
Boltzmann Machines
Autoencoders
XGBoost
R
How to install R
How to Install R Studio on PC
How to Install R Studio on Mac
Data Handling in R Studio
How to Import Libraries to R Studio
How to Import CSV Data in R studio
Regression in R Studio
Simple Linear Regression in R Studio
Multiple Linear Regression in R studio
Polynomial Regression in R Studio
Support Vector Regression (SVR) in R Studio
Decision Tree Regression in R studio
Random Forest Regression in R Studio
Logistic Regression in R Studio
Multivariate Analysis in R Studio
Principal Component Analysis (PCA) in R Studio
Linear Discriminant Analysis (LDA) in R Studio
Classification in R Studio
K-Nearest Neighbors (K-NN) in R Studio
Support Vector Machine (SVM) in R Studio
Kernal SVM in R Studio
Naive Bayes in R studio
Decision Tree Classification in R studio
Random Forest Classification in R Studio
K-Mean Clustering in R Studio
Hierarchical Clustering in R Studio
Association Rule Learning in R Studio
Apriori in R studio
Eclat in R Studio
Simple Artificial Intelligent in R Studio
Upper Confidence Bound (UCB) in R Studio
Thompson Sampling in R Studio
Deep Learning in R Studio
Natural Language Processing (NLP) in R Studio
Artificial Neural Networks (ANN) in R Studio
Convolutional Neural Networks (CNN) in R Studio
Recurrent Neural Networks (RNN) in R Studio
Self-Organizing Maps (SOM) in R Studio
Boltzmann Machines in R Studio
Autoencoders in R Studio
XGBoost in R Studio
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Contact
Convolutional Neural Networks (CNN) in R Studio