Book Image

Artificial Intelligence for Big Data

By : Anand Deshpande, Manish Kumar
Book Image

Artificial Intelligence for Big Data

By: Anand Deshpande, Manish Kumar

Overview of this book

In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data. With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems. By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems.
Table of Contents (19 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Application in Big Data analytics


Frequently, the terms big data and Cognitive Intelligence are used together. Let us understand the relationship between these two concepts. During the course of this book, we have already seen primary aspects and details of big data, such as Volume, Velocity, and Variety. The data volumes are growing exponentially with more devices and systems producing data across business domains and platforms.

As a simple example, a person living in any urban area across the world, is producing at least a few megabytes of data every day with the use of smartphones, televisions, various electronic gadgets, and even cars. These personalized datasets along with industrial and enterprise data assets are adding to the volume of data everyday. This data is generated and stored at an ever increasing velocity into centralized servers on the premise or within the cloud. In order for the data assets to be of value, the analysis and actionable insights should be generated as close...