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Preface Chapter 1: Machine Learning Review Machine learning - history and definition What is not machine learning Machine learning - concepts and terminology Machine learning - types and subtypes Datasets used in machine learning Machine learning applications Practical issues in machine learning Machine learning - roles and process Roles Process Machine learning -tools and datasets Datasets Summary Chapter 2: Practical Approach to Real-World Supervised Learning Formal description and notation Data quality analysis Descriptive data analysis Basic label analysis Basic feature analysis Visualization analysis Univariate feature analysis Multivariate feature analysis Data transformation and preprocessing Feature construction Handling missing values Outliers Discretization Data sampling Is sampling needed Undersampling and oversampling Training, validation, and test set Feature relevance analysis and dimensionality reduction Feature search techniques Feature evaluation techniques Filter approach Wrapper approach Embedded approach Model building Linear models Linear Regression Naive Bayes Logistic Regression Non-linear models Decision Trees K-Nearest Neighbors (KNN) Support vector machines (SVM) Ensemble learning and meta learners Bootstrap aggregating or bagging Boosting Model assessment, evaluation, and comparisons Model assessment Model evaluation metrics Confusion matrix and related metrics ROC and PRC curves Gain charts and lift curves Model comparisons Comparing two algorithms Comparing multiple algorithms Case Study - Horse Colic Classification Business problem Machine learning mapping Data analysis Label analysis Features analysis Supervised learning experiments Weka experiments RapidMiner experiments Results, observations, and analysis Summary References Chapter 3: Unsupervised Machine Learninq Techniques ¡­¡­ Chapter 4: Semi-Supervised and Active Learning Chapter 5: Real-Time Stream Machine Learning Chapter 6: Probabilistic Graph Modeling Chapter 7: Deep Learning Chapter 8: Text Mining and Natural Language Processing Chapter 9: Bia Data Machine Learnina - The Final Frontier Appendix A: Linear Algebra Appendix B: Probability Index