This GitHub repository contains code and datasets for the "Machine Learning A-Z" course. The repository is structured into various parts and sections, each focusing on different topics in machine learning. Here"s an overview of the project structure:

• Data.csv: Dataset for data preprocessing.
• data_preprocessing_template.ipynb: Jupyter Notebook containing a template for data preprocessing.
• data_preprocessing_tools.ipynb: Jupyter Notebook with data preprocessing tools.

• Social_Network_Ads.csv: Dataset for model selection.
• grid_search.ipynb: Jupyter Notebook for grid search.
• k_fold_cross_validation.ipynb: Jupyter Notebook for k-fold cross-validation.

• Data.csv: Dataset for XGBoost.
• xg_boost.ipynb: Jupyter Notebook for XGBoost.

• Salary_Data.csv: Dataset for simple linear regression.
• simple_linear_regression.ipynb: Jupyter Notebook for simple linear regression.

• 50_Startups.csv: Dataset for multiple linear regression.
• multiple_linear_regression.ipynb: Jupyter Notebook for multiple linear regression.

• Position_Salaries.csv: Dataset for polynomial regression.
• polynomial_regression.ipynb: Jupyter Notebook for polynomial regression.

• Position_Salaries.csv: Dataset for support vector regression.
• support_vector_regression.ipynb: Jupyter Notebook for support vector regression.

• Position_Salaries.csv: Dataset for decision tree regression.
• decision_tree_regression.ipynb: Jupyter Notebook for decision tree regression.

• Position_Salaries.csv: Dataset for random forest regression.
• random_forest_regression.ipynb: Jupyter Notebook for random forest regression.

• Social_Network_Ads.csv: Dataset for logistic regression.
• logistic_regression.ipynb: Jupyter Notebook for logistic regression.

• Social_Network_Ads.csv: Dataset for K-NN.
• k_nearest_neighbors.ipynb: Jupyter Notebook for K-NN.

• Social_Network_Ads.csv: Dataset for SVM.
• support_vector_machine.ipynb: Jupyter Notebook for SVM.

• Social_Network_Ads.csv: Dataset for kernel SVM.
• kernel_svm.ipynb: Jupyter Notebook for kernel SVM.

• Social_Network_Ads.csv: Dataset for Naive Bayes.
• naive_bayes.ipynb: Jupyter Notebook for Naive Bayes.

• Social_Network_Ads.csv: Dataset for decision tree classification.
• decision_tree_classification.ipynb: Jupyter Notebook for decision tree classification.

• Social_Network_Ads.csv: Dataset for random forest classification.
• random_forest_classification.ipynb: Jupyter Notebook for random forest classification.

• Mall_Customers.csv: Dataset for K-means clustering.
• k_means_clustering.ipynb: Jupyter Notebook for K-means clustering.

• Mall_Customers.csv: Dataset for hierarchical clustering.
• hierarchical_clustering.ipynb: Jupyter Notebook for hierarchical clustering.

• Market_Basket_Optimisation.csv: Dataset for Apriori algorithm.
• apriori.ipynb: Jupyter Notebook for Apriori algorithm.

• Ads_CTR_Optimisation.csv: Dataset for UCB algorithm.
• ucb.ipynb: Jupyter Notebook for UCB algorithm.

• Ads_CTR_Optimisation.csv: Dataset for Thompson Sampling algorithm.
• thompson_sampling.ipynb: Jupyter Notebook for Thompson Sampling algorithm.

• Restaurant_Reviews.tsv: Dataset for natural language processing.
• natural_language_processing.ipynb: Jupyter Notebook for natural language processing.

• Churn_Modelling.csv: Dataset for artificial neural networks.
• artificial_neural_networks.ipynb: Jupyter Notebook for artificial neural networks.

• cats_and_dogs.zip: Dataset for convolutional neural networks.
• convolutional_neural_networks.ipynb: Jupyter Notebook for convolutional neural networks.

• Google_Stock_Price_Train.csv: Dataset for recurrent neural networks.
• recurrent_neural_networks.ipynb: Jupyter Notebook for recurrent neural networks.

• Credit_Card_Applications.csv: Dataset for self-organizing maps.
• self_organizing_maps.ipynb: Jupyter Notebook for self-organizing maps.

• Movies.dat: Dataset for Boltzmann machines.
• boltzmann_machines.ipynb: Jupyter Notebook for Boltzmann machines.

• Credit_Card_Applications.csv: Dataset for autoencoders.
• autoencoders.ipynb: Jupyter Notebook for autoencoders.

• Wine.csv: Dataset for principal component analysis.
• principal_component_analysis.ipynb: Jupyter Notebook for principal component analysis.

• Wine.csv: Dataset for linear discriminant analysis.
• linear_discriminant_analysis.ipynb: Jupyter Notebook for linear discriminant analysis.

• Social_Network_Ads.csv: Dataset for kernel principal component analysis.
• kernel_principal_component_analysis.ipynb: Jupyter Notebook for kernel principal component analysis.

• Position_Salaries.csv: Dataset for model selection.
• model_selection.ipynb: Jupyter Notebook for model selection.

• Churn_Modelling.csv: Dataset for XGBoost.
• xgboost.ipynb: Jupyter Notebook for XGBoost.

• Churn_Modelling.csv: Dataset for LightGBM.
• lightgbm.ipynb: Jupyter Notebook for LightGBM.

Congratulations on completing the Machine Learning A-Z course! You have gained knowledge and practical experience in various machine learning topics and algorithms. Feel free to revisit any section or topic to reinforce your understanding. Keep exploring and applying machine learning in real-world projects.

If you have any further questions or need assistance, please don"t hesitate to ask. Happy learning!