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Artificial Intelligence for Big Data

Artificial Intelligence for Big Data

By : Anand Deshpande, Manish Kumar, Albenzo Coletta, Giancarlo Zaccone
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Artificial Intelligence for Big Data

Artificial Intelligence for Big Data

5 (2)
By: Anand Deshpande, Manish Kumar, Albenzo Coletta, Giancarlo Zaccone

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 (14 chapters)
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Preface

We are at an interesting juncture in the evolution of the digital age, where there is an enormous amount of computing power and data in the hands of everyone. There has been an exponential growth in the amount of data we now have in digital form. While being associated with data-related technologies for more than 6 years, we have seen a rapid shift towards enterprises  that are willing to leverage data assets initially for insights and eventually for advanced analytics. What sounded like hype initially has become a reality in a very short period of time. Most companies have realized that data is the most important asset needed to stay relevant. As practitioners in the big data analytics industry, we have seen this shift very closely by working with many clients of various sizes, across regions and functional domains. There is a common theme evolving toward open distributed open source computing to store data assets and perform advanced analytics to predict future trends and risks for businesses.

This book is an attempt to share the knowledge we have acquired over time to help new entrants in the big data space to learn from our experience. We realize that the field of artificial intelligence is vast and it is just the beginning of a revolution in the history of mankind. We are going to see AI becoming mainstream in everyone’s life and complementing human capabilities to solve some of the problems that have troubled us for a long time. This book takes a holistic approach into the theory of machine learning and AI, starting from the very basics to building applications with cognitive intelligence. We have taken a simple approach to illustrate the core concepts and theory, supplemented by illustrative diagrams and examples.

It will be encouraging for us for readers to benefit from the book and fast-track their learning and innovation into one of the most exciting fields of computing so they can create a truly intelligent system that will augment our abilities to the next level. 

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Artificial Intelligence for Big Data
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