Machine Learning Models and Algorithms for Big Data Classification: Thinking with Examples for Effective Learning

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.

Table of Contents

Chapter 1 Science of Information

Part I Understanding Big Data
Chapter 2 Big Data Essentials
Chapter 3 Big Data Analytics
Chapter Part II Understanding Big Data Systems
Chapter 4 Distributed File System
Chapter 5 MapReduce Programming Platform

Part III Understanding Machine Learning
Chapter 6 Modeling and Algorithms
Chapter 7 Supervised Learning Models
Chapter 8 Supervised Learning Algorithms
Chapter 9 Support Vector Machine
Chapter 10 Decision Tree Learning

Part IV Understanding Scaling-Up Machine Learning
Chapter 11 Random Forest Learning
Chapter 12 Deep Learning Models
Chapter 13 Chandelier Decision Tree
Chapter 14 Dimensionality Reduction