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

Summary


In this chapter, we learnt about the basic fundamentals of Machine Learning, the different types such as Supervised and Unsupervised and major concepts such as data pre-processing, data imputation, managing imbalanced classes and other topics.

We also learnt about the key distinctions between terms that are being used interchangeably today, in particular the terms AI and Machine Learning. We learned that artificial intelligence deals with a vast array of topics, such as game theory, sociology, constrained optimizations, and machine learning; AI is much broader in scope relative to machine learning.

Machine learning facilitates AI; namely, machine learning algorithms are used to create systems that are artificially intelligent, but they differ in scope. A regression problem (finding the line of best fit given a set of points) can be considered a machine learning algorithm, but it is much less likely to be seen as an AI algorithm (conceptually, although it technically could be).

In the...