Applied Learning Algorithms for Intelligent IoT

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Synopsis

This book vividly illustrates all the promising and potential machine learning (ML) and deep learning (DL) algorithms through a host of real-world and real-time business use cases. Machines and devices can be empowered to self-learn and exhibit intelligent behavior. Also, Big Data combined with real-time and runtime data can lead to personalized, prognostic, predictive, and prescriptive insights. This book examines the following topics:



Cognitive machines and devices


Cyber physical systems (CPS)


The Internet of Things (IoT) and industrial use cases


Industry 4.0 for smarter manufacturing


Predictive and prescriptive insights for smarter systems


Machine vision and intelligence


Natural interfaces


K-means clustering algorithm


Support vector machine (SVM) algorithm


A priori algorithms


Linear and logistic regression

Applied Learning Algorithms for Intelligent IoT clearly articulates ML and DL algorithms that can be used to unearth predictive and prescriptive insights out of Big Data. Transforming raw data into information and relevant knowledge is gaining prominence with the availability of data processing and mining, analytics algorithms, platforms, frameworks, and other accelerators discussed in the book. Now, with the emergence of machine learning algorithms, the field of data analytics is bound to reach new heights.

This book will serve as a comprehensive guide for AI researchers, faculty members, and IT professionals. Every chapter will discuss one ML algorithm, its origin, challenges, and benefits, as well as a sample industry use case for explaining the algorithm in detail. The book’s detailed and deeper dive into ML and DL algorithms using a practical use case can foster innovative research.

Book details

Author:
Pethuru Raj Chelliah, Usha Sakthivel, Susila Nagarajan
ISBN:
9781000461350
Related ISBNs:
9781003119838, 9780367635947, 9781032113210
Publisher:
CRC Press
Pages:
356
Reading age:
Not specified
Includes images:
No
Date of addition:
2021-10-28
Usage restrictions:
Copyright
Copyright date:
2022
Copyright by:
N/A 
Adult content:
No
Language:
English
Categories:
Computers and Internet, Nonfiction