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

Chapter 2. Ontology for Big Data

In the introductory chapter, we learned that big data has fueled rapid advances in the field of artificial intelligence. This is primarily because of the availability of extremely large datasets from heterogeneous sources and exponential growth in processing power due to distributed computing. It is extremely difficult to derive value from large data volumes if there is no standardization or a common language for interpreting data into information and converting information into knowledge. For example, two people who speak two different languages, and do not understand each other's languages, cannot get into a verbal conversation unless there is some translation mechanism in between. Translations and interpretations are possible only when there is a semantic meaning associated with a keyword and when grammatical rules are applied as conjunctions. As an example, here is a sentence in the English and Spanish languages:

Broadly, we can break a sentence down in...