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Machine Learning Models and Algorithms for Big Data Classification


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Table of Contents

Science of Information.- Part I Understanding Big Data.- Big Data Essentials.- Big Data Analytics.- Part II Understanding Big Data Systems.- Distributed File System.- MapReduce Programming Platform.- Part III Understanding Machine Learning.- Modeling and Algorithms.- Supervised Learning Models.- Supervised Learning Algorithms.- Support Vector Machine.- Decision Tree Learning.- Part IV Understanding Scaling-Up Machine Learning.- Random Forest Learning.- Deep Learning Models.- Chandelier Decision Tree.- Dimensionality Reduction.

About the Author

Shan Suthaharan is a Professor of Computer Science at the University of North Carolina at Greensboro (UNCG), North Carolina, USA. He also serves as the Director of Undergraduate Studies at the Department of Computer Science at UNCG. He has more than twenty-five years of university teaching and administrative experience, and has taught both undergraduate and graduate courses. His aspiration is to educate and train students so that they can prosper in the computer field by understanding current real-world and complex problems, and develop efficient techniques and technologies. His current teaching interests include big data analytics and machine learning, cryptography and network security, and computer networking and analysis. He earned his doctorate in Computer Science from Monash University, Australia. Since then, he has been actively working on disseminating his knowledge and experience through teaching, advising, seminars, research, and publications. Dr. Suthaharan enjoys investigating real-world, complex problems, and developing and implementing algorithms to solve those problems using modern technologies. The main theme of his current research is the signature discovery and event detection for a secure and reliable environment. The ultimate goal of his research is to build a secure and reliable environment using modern and emerging technologies. His current research primarily focuses on the characterization and detection of environmental events, the exploration of machine learning techniques, and the development of advanced statistical and computational techniques to discover key signatures and detect emerging events from structured and unstructured big data. Dr. Suthaharan has authored or co-authored more than seventy-five research papers in the areas of computer science, and published them in international journals and referred conference proceedings. He also invented a key management and encryption technology, which has been patented in Australia, Japan, and Singapore. He also received visiting scholar awards from and served as a visiting researcher at the University of Sydney, Australia; the University of Melbourne, Australia; and the University of California, Berkeley, USA. He was a senior member of the Institute of Electrical and Electronics Engineers, and volunteered as an elected chair of the Central North Carolina Section twice. He is a member of Sigma Xi, the Scientific Research Society, and a Fellow of the Institution of Engineering and Technology.


"This book is a good introduction to machine learning models for big data classification ... . Typical of a Springer book, this one is concise, clear, and well organized. ... each chapter contains programming examples and references ... . this book is useful if you want to know more about machine learning models and algorithms for big data classification." (J. Myerson, Computing Reviews, February, 2016)

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