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 understood the concept of the results pyramid, which is a model for the continuous improvement of human life and striving to get better results with an improved understanding of the world based on data (experiences), which shape our models (beliefs). With the convergence of the evolving human brain and computers, we know that the best of both worlds can really improve our lives. We have seen how computers have evolved from dumb to intelligent machines and we provided a high-level overview of intelligence and Big Data, along with types of processing frameworks.

With this introduction and context, in subsequent chapters in this book, we are going to take a deep dive into the core concepts of taking an algorithmic approach to data and the basics of machine learning with illustrative algorithms. We will implement these algorithms with available frameworks and illustrate this with code samples.