├── Association Rule Learning ├── Section 28 - Apriori │ ├── Python │ │ ├── Market_Basket_Optimisation.csv │ │ ├── apriori.ipynb │ │ └── apriori.py │ └── R │ │ ├── Market_Basket_Optimisation.csv │ │ └── apriori.R └── Section 29 - Eclat │ ├── Python │ ├── Market_Basket_Optimisation.csv │ ├── eclat.ipynb │ └── eclat.py │ └── R │ ├── Market_Basket_Optimisation.csv │ └── eclat.R ├── Classification ├── Section 14 - Logistic Regression │ ├── Python │ │ ├── Social_Network_Ads.csv │ │ ├── logistic_regression.ipynb │ │ └── logistic_regression.py │ └── R │ │ ├── Social_Network_Ads.csv │ │ └── logistic_regression.R ├── Section 15 - K-Nearest Neighbors (K-NN) │ ├── Python │ │ ├── Social_Network_Ads.csv │ │ ├── k_nearest_neighbors.ipynb │ │ └── k_nearest_neighbors.py │ └── R │ │ ├── Social_Network_Ads.csv │ │ ├── classification_template.R │ │ └── knn.R ├── Section 16 - Support Vector Machine (SVM) │ ├── Python │ │ ├── Social_Network_Ads.csv │ │ ├── support_vector_machine.ipynb │ │ └── support_vector_machine.py │ └── R │ │ ├── Social_Network_Ads.csv │ │ ├── classification_template.R │ │ └── svm.R ├── Section 17 - Kernel SVM │ ├── Python │ │ ├── Social_Network_Ads.csv │ │ ├── kernel_svm.ipynb │ │ └── kernel_svm.py │ └── R │ │ ├── Social_Network_Ads.csv │ │ ├── classification_template.R │ │ └── kernel_svm.R ├── Section 18 - Naive Bayes │ ├── Python │ │ ├── Social_Network_Ads.csv │ │ ├── naive_bayes.ipynb │ │ └── naive_bayes.py │ └── R │ │ ├── Social_Network_Ads.csv │ │ ├── classification_template.R │ │ └── naive_bayes.R ├── Section 19 - Decision Tree Classification │ ├── Python │ │ ├── Social_Network_Ads.csv │ │ ├── decision_tree_classification.ipynb │ │ └── decision_tree_classification.py │ └── R │ │ ├── Social_Network_Ads.csv │ │ ├── classification_template.R │ │ └── decision_tree_classification.R └── Section 20 - Random Forest Classification │ ├── Python │ ├── Social_Network_Ads.csv │ ├── random_forest_classification.ipynb │ └── random_forest_classification.py │ └── R │ ├── Social_Network_Ads.csv │ ├── classification_template.R │ └── random_forest_classification.R ├── Classification_logistic_Regression.ipynb ├── Clustering ├── Section 24 - K-Means Clustering │ ├── Python │ │ ├── Mall_Customers.csv │ │ ├── k_means_clustering.ipynb │ │ └── k_means_clustering.py │ └── R │ │ ├── Mall_Customers.csv │ │ ├── data_preprocessing_template.R │ │ └── k_means_clustering.R └── Section 25 - Hierarchical Clustering │ ├── Python │ ├── Mall_Customers.csv │ ├── hierarchical_clustering.ipynb │ └── hierarchical_clustering.py │ └── R │ ├── Mall_Customers.csv │ ├── data_preprocessing_template.R │ └── hc.R ├── Data Preprocessing └── Section 2 -------------------- Part 1 - Data Preprocessing -------------------- │ ├── Python │ ├── Data.csv │ ├── data_preprocessing_template.ipynb │ ├── data_preprocessing_template.py │ ├── data_preprocessing_tools.ipynb │ └── data_preprocessing_tools.py │ └── R │ ├── Data.csv │ ├── categorical_data.R │ ├── data_preprocessing_template.R │ └── missing_data.R ├── Data_Preprocessing_Tools.ipynb ├── Data_preprocessing_template.ipynb ├── Decision_Tree_Classification.ipynb ├── Decision_Tree_Regression.ipynb ├── Deep Learning ├── Section 39 - Artificial Neural Networks (ANN) │ ├── Python │ │ ├── Churn_Modelling.csv │ │ ├── artificial_neural_network.ipynb │ │ └── artificial_neural_network.py │ ├── R │ │ ├── Churn_Modelling.csv │ │ └── ann.R │ └── Stochastic_Gradient_Descent.png └── Section 40 - Convolutional Neural Networks (CNN) │ └── Python │ ├── convolutional_neural_network.ipynb │ └── convolutional_neural_network.py ├── Dimensionality Reduction ├── Section 43 - Principal Component Analysis (PCA) │ ├── Python │ │ ├── Wine.csv │ │ ├── principal_component_analysis.ipynb │ │ └── principal_component_analysis.py │ └── R │ │ ├── Wine.csv │ │ └── pca.R ├── Section 44 - Linear Discriminant Analysis (LDA) │ ├── Python │ │ ├── Wine.csv │ │ ├── linear_discriminant_analysis.ipynb │ │ └── linear_discriminant_analysis.py │ └── R │ │ ├── Wine.csv │ │ └── lda.R └── Section 45 - Kernel PCA │ ├── Python │ ├── Wine.csv │ ├── kernel_pca.ipynb │ └── kernel_pca.py │ └── R │ ├── Social_Network_Ads.csv │ └── kernel_pca.R ├── Eclat.ipynb ├── K_Nearest_Neighbors_(K_NN).ipynb ├── K_means_clustering.ipynb ├── Kernel_SVM.ipynb ├── Machine_Learning_A_Z_Q_A.pdf ├── Model Selection _ Boosting ├── Section 48 - Model Selection │ ├── Python │ │ ├── Social_Network_Ads.csv │ │ ├── grid_search.ipynb │ │ ├── grid_search.py │ │ ├── k_fold_cross_validation.ipynb │ │ └── k_fold_cross_validation.py │ └── R │ │ ├── Social_Network_Ads.csv │ │ ├── grid_search.R │ │ └── k_fold_cross_validation.R └── Section 49 - XGBoost │ ├── Python │ ├── Data.csv │ ├── xg_boost.ipynb │ └── xg_boost.py │ └── R │ ├── Churn_Modelling.csv │ └── xg_boost.R ├── Multiple_Linear_Regression_.ipynb ├── Naive_Bayes.ipynb ├── Natural Language Processing └── Section 36 - Natural Language Processing │ ├── Python │ ├── Restaurant_Reviews.tsv │ ├── natural_language_processing.ipynb │ └── natural_language_processing.py │ └── R │ ├── Restaurant_Reviews.tsv │ └── natural_language_processing.R ├── Polynomial_Regression.ipynb ├── README.md ├── Random_Forest_Classification.ipynb ├── Random_Forest_Regression_.ipynb ├── Regression ├── Section 4 - Simple Linear Regression │ ├── Python │ │ ├── Salary_Data.csv │ │ ├── simple_linear_regression.ipynb │ │ └── simple_linear_regression.py │ └── R │ │ ├── Salary_Data.csv │ │ ├── data_preprocessing_template.R │ │ └── simple_linear_regression.R ├── Section 5 - Multiple Linear Regression │ ├── Python │ │ ├── 50_Startups.csv │ │ ├── multiple_linear_regression.ipynb │ │ └── multiple_linear_regression.py │ └── R │ │ ├── 50_Startups.csv │ │ ├── data_preprocessing_template.R │ │ └── multiple_linear_regression.R ├── Section 6 - Polynomial Regression │ ├── Python │ │ ├── Position_Salaries.csv │ │ ├── polynomial_regression.ipynb │ │ └── polynomial_regression.py │ └── R │ │ ├── Position_Salaries.csv │ │ ├── data_preprocessing_template.R │ │ └── polynomial_regression.R ├── Section 7 - Support Vector Regression (SVR) │ ├── Python │ │ ├── Position_Salaries.csv │ │ ├── support_vector_regression.ipynb │ │ └── support_vector_regression.py │ └── R │ │ ├── Position_Salaries.csv │ │ ├── data_preprocessing_template.R │ │ └── svr.R ├── Section 8 - Decision Tree Regression │ ├── Python │ │ ├── Position_Salaries.csv │ │ ├── decision_tree_regression.ipynb │ │ └── decision_tree_regression.py │ └── R │ │ ├── Position_Salaries.csv │ │ ├── decision_tree_regression.R │ │ └── regression_template.R └── Section 9 - 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