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Machine Learning Models and Algorithms for Big Data Classification Thinking with Examples for Effective Learning
Author
Title
Machine Learning Models and Algorithms for Big Data Classification [electronic resource] : Thinking with Examples for Effective Learning / by Shan Suthaharan.
ISBN
9781489976413
Edition
1st ed. 2016.
Publication
Boston, MA : Springer US : Imprint: Springer, 2016.
Physical Description
XIX, 359 p. 149 illus., 82 illus. in color : online resource.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learning (a layered approach) are highly suitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learning, to gain a quick understanding of the techniques and technologies; therefore, the theory, examples, and programs (Matlab and R) presented in this book have been simplified, hardcoded, repeated, or spaced for improvements. They provide vehicles to test and understand the complicated concepts of various topics in the field. It is expected that the readers adopt these programs to experiment with the examples, and then modify or write their own programs toward advancing their knowledge for solving more complex and challenging problems. The presentation format of this book focuses on simplicity, readability, and dependability so that both undergraduate and graduate students as well as new researchers, developers, and practitioners in this field can easily trust and grasp the concepts, and learn them effectively. It has been written to reduce the mathematical complexity and help the vast majority of readers to understand the topics and get interested in the field. This book consists of four parts, with the total of 14 chapters. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The second part covers the topics that can explain the systems required for processing big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Finally, the fourth part concentrates on the topics that explain the scaling-up machine learning, an important solution for modern big data problems.
Variant and related titles
Springer ebooks.
Other formats
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
December 09, 2015
Series
Integrated Series in Information Systems, 36
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.
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