Applied Learning Algorithms for Intelligent IoT

• 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.