Cutting-edge machine learning principles, practices, and applications
This comprehensive textbook explores the theoretical under¬pinnings of learning and equips readers with the knowledge needed to apply powerful machine learning techniques to solve challenging real-world problems. Applied Machine Learning shows, step by step, how to conceptualize problems, accurately represent data, select and tune algorithms, interpret and analyze results, and make informed strategic decisions. Presented in a non-rigorous mathematical style, the book covers a broad array of machine learning topics with special emphasis on methods that have been profitably employed.
Coverage includes:
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Supervised learning
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Statistical learning
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Learning with support vector machines (SVM)
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Learning with neural networks (NN)
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Fuzzy inference systems
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Data clustering
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Data transformations
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Decision tree learning
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Business intelligence
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Data mining
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And much more
Dedication
Contents
Preface
Acknowledgements
Chapter 1 : Introduction
Chapter 2 : Supervised Learning: Rationale and Basics
Chapter 3 : Statistical Learning
Chapter 4 : Learning With Support Vector Machines (SVM)
Chapter 5 : Learning With Neural Networks (NN)
Chapter 6 : Fuzzy Inference Systems
Chapter 7 : Data Clustering and Data Transformations
Chapter 8 : Decision Tree Learning
Chapter 9 : Business Intelligence and Data Mining : Techniques and Applications
Appendix A Genetic Algorithm (GA) For Search Optimization
Appendix B Reinforcement Learning (RL)
Index
