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

Summary


In this chapter, we have introduced the concept of genetic algorithms (GAs) and programming constructs related to GAs. These algorithms derive inspiration from the natural process of evolution. Living species evolve by inheritance, variation in partner selection, and hence attributes of the offspring and occasional (random) mutation in the genetic code (DNA structure). The same concepts are applied in the GAs in order to search the best possible solution from a vast space of possible options. The algorithm is best applied to problems where brute force is insufficient and cannot reach a solution within a reasonable time.

We have seen the structure of GAs in general and implemented a solution for a simple problem in Java. We have reviewed some of the features of the KEEL framework and how it is very easy to translate data into knowledge. KEEL is a Java-based desktop application that facilitates the analysis of the behavior of evolutionary learning in different areas of learning and...