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.
