Fundamentals of Machine Learning for Predictive Data Analytics, second edition

The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice.Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.

I Introduction to Machine Learning and Data Analytics 1 Machine Learning for Predictive Data Analytics 2 Data to Insights to Decisions 3 Data Exploration II Predictive Data Analytics 4 Information-Based Learning 5 Similarity-Based Learning 6 Probability-Based Learning 7 Error-Based Learning 8 Deep Learning 9 Evaluation III Beyond Prediction 10 Beyond Prediction: Unsupervised Learning 11 Beyond Prediction: Reinforcement Learning IV Case Studies and Conclusions 12 Case Study: Customer Churn 13 Case Study: Galaxy Classification 14 The Art of Machine Learning for Predictive Data Analytics V Appendices A Descriptive Statistics and Data Visualization for Machine Learning B Introduction to Probability for Machine Learning C Differentiation Techniques for Machine Learning D Introduction to Linear Algebra Bibliography Index