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 3. Learning from Big Data

In the first two chapters, we set the context for intelligent machines with the big data revolution and how big data is fueling rapid advances in artificial intelligence. We also emphasized the need for a global vocabulary for universal knowledge representation. We have also seen how that need is fulfilled with the use of ontologies and how ontologies help construct a semantic view of the world.

The quest is for the knowledge, which is derived from information, which is in turn derived from the vast amounts of data that we are generating. Knowledge facilitates a rational decision-making process for machines that complements and augments human capabilities. We have seen how the Resource Description Framework (RDF) provides the schematic backbone for the knowledge assets along with Web Ontology Language (OWL) fundamentals and the query language for RDFs (SPARQL).

In this chapter, we are going to look at some of the basic concepts of machine learning and take...