Machine Learning Fundamentals : Concepts, Models, and Applications

Front cover -- Half title -- Title -- Copyright -- Description -- Contents -- Preface -- Acknowledgments -- Introduction -- Primary Audience -- Scope -- PART 1 Data Science and Its Elements -- CHAPTER 1 Data Science and Machine Learning (ML) An Overview and Scope of Data Science -- Chapter Highlights -- Introduction -- What Is Data Science? -- Role of Statistics in Data Science -- Conflicting Definitions of Data Science and Its Relation to Statistics -- A Brief History and Predictions for Data Science -- Difference Between Data Science and Data Analytics? -- Knowledge and Skills for Data Science Professionals -- Broad View of Data Science and Body of Knowledge -- Data Science and Machine Learning (ML) -- Application Areas of Machine Learning (ML) -- Career Path for Data Science Professionals and Data Scientists -- Outlook -- Summary -- CHAPTER 2 Introduction and Basics of Machine Learning (ML) -- What Is Machine Learning (ML)? -- Why Machine Learning? -- Machine Learning Problem-Solving Approach -- Supervised Learning -- Unsupervised Learning -- Difference Between Supervised and Unsupervised Learning -- Overview of Machine Learning Models -- Regression -- Simple and Multiple Regression Models -- Other Regression Models -- Classification -- Different Types of Classification Tasks in Machine Learning -- Binary Classification -- MultiClass Classification -- Commonly Used Binary Classification Algorithms -- Types of Naïve Bayes Classifier -- Support Vector Machines (SVM) -- The K-Nearest Neighbor -- Random Forest Algorithm -- Neural Networks -- Deep Learning [8] -- Unsupervised Learning -- Common Unsupervised Learning Approaches -- Clustering -- Types of Clustering Methods -- Exclusive and Overlapping Clustering -- K-Means Clustering -- Hierarchical Clustering -- Mean Shift Clustering Algorithm -- Gaussian Mixture Model -- Association.