Applied Learning Algorithms for Intelligent Iot

Applied Learning Algorithms for Intelligent IoT 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 such topics as:

• Cognitive machines and devices
• Cyber physical systems (CPS)
• The Internet of Things (IoT) and industrial use cases
• The 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 algorithm
• Linear and logistic regression.

The book clearly articulates ML and DL that algorithms can be used to unearth predictive and prescriptive insights out of big data. Transforming raw data to information and to 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 newer heights.

This cutting-edge reference will serve as a comprehensive guide for AI researchers, faculty members, and IT professionals. Every chapter will discuss about one ML algorithm, its origin, challenges and benefits, 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.