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Applied Learning Algorithms for Intelligent IoT (Hardcover)
Loot Price: R3,508
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Applied Learning Algorithms for Intelligent IoT (Hardcover)
Expected to ship within 12 - 17 working days
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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.
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