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

Ontology of information science


Formally, the Ontology of information sciences is defined as: A formal naming and definition of types, properties, and interrelationships of the entities that fundamentally exist for a particular domain.

There is a fundamental difference between people and computers when it comes to dealing with information. For computers, information is available in the form of strings whereas for humans, the information is available in the form of things. Let's understand the difference between strings and things. When we add metadata to a string, it becomes a thing. Metadata is data about data (the string in this case) or  contextual information about data. The idea is to convert the data into knowledge. The following illustration gives us a good idea about how to convert data into knowledge:

The text or the number 66 is Data; in itself, 66 does not convey any meaning. When we say 66F, 66 becomes a measure of temperature and at this point it represents some Information...