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It is structured the following way: Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression, Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification, Part 4 - Clustering: K-Means, Hierarchical Clustering, Part 5 - Association Rule Learning: Apriori, Eclat, Part 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling, Part 7 - Natural Language Processing: Bag-of-words model and algorithms for NLP, Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks, Part 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA, Part 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost. 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