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The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice.

Table of 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 Introduction to Probability for Machine Learning
C Differentiation Techniques for Machine Learning
D Introduction to Linear Algebra
Bibliography
Index

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.