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

Data dimensionality reduction


So far in this chapter, we have looked at the basic concepts of supervised and unsupervised learning with the simplest possible examples. In these examples, we have considered a limited number of factors that contribute to the outcome. However, in the real world, we have a very large number of data points that are available for analysis and model generation. Every additional factor adds one dimension within the space, and beyond the third dimension, it becomes difficult to effectively visualize the data in a conceivable form. With each new dimension, there is a performance impact on the model generation exercise.

In the world of big data, where we now have the capability to bring in data from heterogeneous data sources, which was not possible earlier, we are constantly adding more dimensions to our datasets. While it is great to have additional data points and attributes to better understand a problem, more is not always better if we consider the computational...