Machine learning in cognitive IoT / by Neeraj Kumar, Aaisha Makkar.
By: Kumar, Neeraj (Computer scientist) [author.]
.
Contributor(s): Makkar, Aaisha [author,].
Material type: 
BookPublisher: Boca Raton, FL : CRC Press, Taylor & Francis Group, [2020]Description: 1 online resource (xxii, 296 pages) : illustrations.Content type: text Media type: computer Carrier type: online resourceISBN: 9781000767971; 1000767973; 9780429342615; 0429342616; 9781000767599; 1000767590.Subject(s): Embedded computer systems | Machine learning | Internet of things | COMPUTERS / Machine Theory | COMPUTERS / Computer Engineering | MATHEMATICS / GeneralDDC classification: 006.22 Online resources: Taylor & Francis | OCLC metadata license agreement Summary: This book covers the different technologies of Internet, and machine learning capabilities involved in Cognitive Internet of Things (CIoT). Machine learning is explored by covering all the technical issues and various models used for data analytics during decision making at different steps. It initiates with IoT basics, its history, architecture and applications followed by capabilities of CIoT in real world and description of machine learning (ML) in data mining. Further, it explains various ML techniques and paradigms with different phases of data pre-processing and feature engineering. Each chapter includes sample questions to help understand concepts of ML used in different applications. Explains integration of Machine Learning in IoT for building an efficient decision support system Covers IoT, CIoT, machine learning paradigms and models Includes implementation of machine learning models in R Help the analysts and developers to work efficiently with emerging technologies such as data analytics, data processing, Big Data, Robotics Includes programming codes in Python/Matlab/R alongwith practical examples, questions and multiple choice questions
This book covers the different technologies of Internet, and machine learning capabilities involved in Cognitive Internet of Things (CIoT). Machine learning is explored by covering all the technical issues and various models used for data analytics during decision making at different steps. It initiates with IoT basics, its history, architecture and applications followed by capabilities of CIoT in real world and description of machine learning (ML) in data mining. Further, it explains various ML techniques and paradigms with different phases of data pre-processing and feature engineering. Each chapter includes sample questions to help understand concepts of ML used in different applications. Explains integration of Machine Learning in IoT for building an efficient decision support system Covers IoT, CIoT, machine learning paradigms and models Includes implementation of machine learning models in R Help the analysts and developers to work efficiently with emerging technologies such as data analytics, data processing, Big Data, Robotics Includes programming codes in Python/Matlab/R alongwith practical examples, questions and multiple choice questions
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