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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Machine Learning Models and Algorithms for Big Data Classification - Thinking with Examples for Effective Learning (Hardcover, 1st ed. 2016)
Loot Price: R4,828
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Machine Learning Models and Algorithms for Big Data Classification - Thinking with Examples for Effective Learning (Hardcover, 1st ed. 2016)
Series: Integrated Series in Information Systems, 36
Expected to ship within 12 - 19 working days
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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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