Machine Learning Models and Algorithms for Big Data Classification

Thinking with Examples for Effective Learning

Autor: Shan Suthaharan

Język:

Oprawa: Miękka

Wydawca: Springer-Verlag New York Inc.

Dostępność: Dostępna u dostawcy

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This book presents machine learning models and algorithms to address big data classification problem...

Informacje o książce

Autor

Shan Suthaharan

Język

Angielski

Oprawa

Książka - Miękka

Data wydania

2016

strony

359

EAN

9781489978523

ISBN

1489978526

Enbook ID

18049805

Wydawca

Springer-Verlag New York Inc.

Waga

5737

Wymiary

155 x 235 x 22

Pełny opis

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.

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Machine Learning Models and Algorithms for Big Data Classification

Thinking with Examples for Effective Learning

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