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 6. Natural Language Processing

Machine learning, or artificial intelligence, is based on data that can be structured or unstructured. Natural language processing (NLP) is an area of algorithms that is focused on processing unstructured data. This chapter is focused on unstructured data with a natural language text format. Organizations always have large corpuses of unstructured text data, either in the form of word documents, PDFs, email body, or web documents. With advances in technology, organizations have started relying on large volumes of text information. For example, a legal firm has lots of information in the form of bond papers, legal agreements, court orders, law documents, and so on. Such information assets are made up of textual information that is domain-specific (legal in this case). It is imperative that in order to utilize these valuable textual assets, and convert the information into knowledge, we require intelligent machines to be able to understand the text as...