IntroductionMachine LearningUnsupervised LearningHierarchical ClusteringExercise 3.01: Performing Hierarchical Clusteringk-means ClusteringExercise 3.02: Implementing k-means ClusteringSupervised LearningClassificationLogistic RegressionExercise 3.03: Text Classification – Logistic RegressionNaive Bayes ClassifiersExercise 3.04: Text Classification – Naive Bayesk-nearest NeighborsExercise 3.05: Text Classification Using the k-nearest Neighbors MethodRegressionLinear RegressionExercise 3.06: Regression Analysis Using Textual DataTree MethodsExercise 3.07: Tree-Based Methods – Decision TreeRandom ForestGradient Boosting Machine and Extreme Gradient BoostExercise 3.08: Tree-Based Methods – Random ForestExercise 3.09: Tree-Based Methods – XGBoostSamplingExercise 3.10: Sampling (Simple Random, Stratified, and Multi-Stage)Developing a Text ClassifierFeature ExtractionFeature EngineeringRemoving Correlated FeaturesExercise 3.11: Removing Highly Correlated Features (Tokens)Dimensionality ReductionExercise 3.12: Performing Dimensionality Reduction Using Principal Component AnalysisDeciding on a Model TypeEvaluating the Performance of a ModelExercise 3.13: Calculating the RMSE and MAPE of a DatasetActivity 3.01: Developing End-to-End Text ClassifiersBuilding Pipelines for NLP ProjectsExercise 3.14: Building the Pipeline for an NLP ProjectSaving and Loading ModelsExercise 3.15: Saving and Loading ModelsSummary