Book Image

Practical Big Data Analytics

By : Nataraj Dasgupta
Book Image

Practical Big Data Analytics

By: Nataraj Dasgupta

Overview of this book

Big Data analytics relates to the strategies used by organizations to collect, organize, and analyze large amounts of data to uncover valuable business insights that cannot be analyzed through traditional systems. Crafting an enterprise-scale cost-efficient Big Data and machine learning solution to uncover insights and value from your organization’s data is a challenge. Today, with hundreds of new Big Data systems, machine learning packages, and BI tools, selecting the right combination of technologies is an even greater challenge. This book will help you do that. With the help of this guide, you will be able to bridge the gap between the theoretical world of technology and the practical reality of building corporate Big Data and data science platforms. You will get hands-on exposure to Hadoop and Spark, build machine learning dashboards using R and R Shiny, create web-based apps using NoSQL databases such as MongoDB, and even learn how to write R code for neural networks. By the end of the book, you will have a very clear and concrete understanding of what Big Data analytics means, how it drives revenues for organizations, and how you can develop your own Big Data analytics solution using the different tools and methods articulated in this book.
Table of Contents (16 chapters)
Title Page
Packt Upsell
Contributors
Preface

Categories of machine learning


Arthur Samuel coined the term machine learning in 1959 while at IBM. A popular definition of machine learning is due to Arthur, who, it is believed, called machine learning a field of computer science that gives computers the ability to learn without being explicitly programmed.

Tom Mitchell, in 1998, added a more specific definition to machine learning and called it a, study of algorithms that improve their performance P at some task T with experience E.

A simple explanation would help to illustrate this concept. By now, most of us are familiar with the concept of spam in emails. Most email accounts also contain a separate folder known as Junk, Spam, or a related term. A cursory check of the folders will usually indicate the presence of several emails, many of which were presumably unsolicited and contain meaningless information.

The mere task of categorizing emails as spam and moving them to a folder involves the application of machine learning. Andrew Ng highlighted...