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.

The book is accessible, offering nontechnical explanations of the ideas underpinning each approach before introducing mathematical models and algorithms. It is focused and deep, providing students with detailed knowledge on core concepts, giving them a solid basis for exploring the field on their own. Both early chapters and later case studies illustrate how the process of learning predictive models fits into the broader business context. The two case studies describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book can be used as a textbook at the introductory level or as a reference for professionals.

Contents

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. Introduct ion to Probability for Machine Learning

C. Differentiation Techniques for Machine Learning

D. Introduct ion to Linear Algebra

About the Author

John D. Kelleher is Academic Leader of the Information, Communication, and Entertainment Research Institute at Technological University Dublin. He is the coauthor of Data Science and the author of Deep Learning, both in the MIT Press Essential Knowledge series.

Brian Mac Namee is Associate Professor at the School of Computer Science at University College Dublin

Aoife D'Arcy is CEO of Krisolis, a data analytics company based in Dublin.