
Machine Learning for Business Analytics
Concepts, Techniques, and Applications with Analytic Solver Data Mining
- English
- PDF
- Available on iOS & Android
Machine Learning for Business Analytics
Concepts, Techniques, and Applications with Analytic Solver Data Mining
About this book
Machine learning—also known as data mining or predictive analytics—is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information.
Machine Learning for Business Analytics: Concepts, Techniques, and Applications with Analytic Solver® Data Mining provides a comprehensive introduction and an overview of this methodology. The fourth edition of this best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, time series forecasting and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques.
This fourth edition of Machine Learning for Business Analytics also includes:
- An expanded chapter on deep learning
- A new chapter on experimental feedback techniques, including A/B testing, uplift modeling, and reinforcement learning
- A new chapter on responsible data science
- Updates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their students
- A full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques
- End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented
- A companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions
This textbook is an ideal resource for upper-level undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.
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Information
Table of contents
- Cover
- Title Page
- Copyright
- Contents
- Foreword
- Preface to the Fourth Edition
- Acknowledgments
- PART I Preliminaries
- PART II Data Exploration and Dimension Reduction
- PART III Performance Evaluation
- PART IV Prediction and Classification Methods
- PART V Intervention and User Feedback
- PART VI Mining Relationships Among Records
- PART VII Forecasting Time Series
- PART VIII Data Analytics
- PART IX Cases
- References
- Data Files Used in the Book
- Index
- EULA