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Ontology for Big Data

In the introductory chapter, we learned that big data has fueled rapid advances in the field of artificial intelligence. This is primarily because of the availability of extremely large datasets from heterogeneous sources and exponential growth in processing power due to distributed computing. It is extremely difficult to derive value from large data volumes if there is no standardization or a common language for interpreting data into information and converting information into knowledge. For example, two people who speak two different languages, and do not understand each other's languages, cannot get into a verbal conversation unless there is some translation mechanism in between. Translations and interpretations are possible only when there is a semantic meaning associated with a keyword and when grammatical rules are applied as conjunctions. As...

Key benefits

Implement AI techniques to build smart applications using Deeplearning4j

Perform big data analytics to derive quality insights using Spark MLlib

Create self-learning systems using neural networks, NLP, and reinforcement learning

Description

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.

Who is this book for?

This book is for you if you are a data scientist, big data professional, or novice who has basic knowledge of big data and wish to get proficiency in Artificial Intelligence techniques for big data. Some competence in mathematics is an added advantage in the field of elementary linear algebra and calculus.

What you will learn

Manage Artificial Intelligence techniques for big data with Java

Build smart systems to analyze data for enhanced customer experience

Learn to use Artificial Intelligence frameworks for big data

Understand complex problems with algorithms and Neuro-Fuzzy systems

Design stratagems to leverage data using Machine Learning process

Apply Deep Learning techniques to prepare data for modeling

Construct models that learn from data using open source tools

Analyze big data problems using scalable Machine Learning algorithms

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

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Anway Jun 27, 2018

Artificial Intelligence for Big Data: Complete guide to automating Big Data solutions using Artificial Intelligence techniques This book bridges a big gap between today's technology and tomorrow's outlook and enables technologists to take a leap into the future. Ideal for developers, designers, data science enthusiast to get started into the journey of Artificial intelligence in the world of big data. Anand very articulately carries the reader right from the beginning concepts of big data, artificial intelligence, machine learning, natural language processing, using statistical methods, etc. into the very intricate details of the technology. Anand has also included snippets of sample code in almost every chapter, while explaining the practical usage of every aspect of the theories he puts forward. The author has kept the book structure very simple with starting from absolute basics of the concept as observed in day-to-day life, moving on to how technology takes inspiration from life, moving on how technology can solve those life problems. At the end of every chapter, the author gives FAQs about the chapter, which basically serve as a synopsis of the chapter, giving the reader a quick revision of what he/she learned in the chapter. And after the FAQs, the author ends the chapter summarizing what was discussed in that chapter and quickly sets up the context of the next chapter.

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Avadhoot Aug 24, 2018

This is very handy book for data engineers. With the advances in Machine Learning and AI, almost all applications are now getting powered by ML. Typically, there are data scientists who study the data and put together the representative models using Python + Jupyter combination. However, when these models (building and execution) need to be put into production, the traditional 'Data Engineers' need to rewrite this whole code in Big Data technologies and also be responsible for its lifecycle management. What I like about this book is that it explains all the concepts (data acquisition, representation, storage, preprocessing, ML algorithms, accuracy measurement, parameter tuning, pipelining etc) keeping Big Data eco system in mind. All the code examples use Spark and SparkML and implemented in Java which are the most reliable and performant technologies for Big Data Analytics.The other good thing about this book is that it explains the theory in a very systematic manner but does not stop at theory. Each chapter has several practical examples and FAQs which are very useful too. Finally, the whole process is effectively explained using the Cyber Security as a case study.I think the best use of this book is to 1. Scan through it once to understand the whole gambit of things 2. Keep it as a reference book when you are solving some real problems using Big Data, AI and ML.

Artificial Intelligence for Big Data: Complete guide to automating Big Data solutions using Artificial Intelligence techniques

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Artificial Intelligence for Big Data

Ontology for Big Data

Human brain and Ontology

Ontology of information science

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