DeepThinking Machine Intelligence HumanCreativity
 

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.

Editorial Reviews
ReviewFrom the Back Cover

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.

April 12, 2016
I purchased the book in order to learn a bit more on, big data and machine learning. This is a nicely written book with all of the fundamental concepts in big data and machine learning. With every concept clearly explained with examples and graphs, accompanied with R codes.

An example is the Patterns of Big data in Section 3.3, where the author explained different pattern evolutions are to be used for supervised learning. As the selected features increase, class separation by the standardization is then clearly demonstrated to be an efficient and accurate method. The zoomed-in figure later shows the majority of one subdomain (red) with just a few others blue dots here and there. Readers can quickly grasp the idea from such masterly explanations.

Overall, the book covers most of the state-of-the-art concepts and techniques, including big data essentials and analytics, MapReduce programming, supervised learning models/algorithms, support vector machine, decision tree learning, random forest learning and deep learning, etc.

If anything, I wish the part II (distributed file system and MapReduce platform) had been explained as, the last chapters, with more examples, to the random forest and deep learning techniques. But such might be too much to ask and might lengthen the book significantly (to the level of being too long).

Strongly recommended to other casual or dedicated readers who are interested in big data and machine learning.

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