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
Chapter 1: Internet of Things Chapter 2: Cognitive Internet of Things Chapter 3: Data mining in IoT Chapter 4: Machine Learning Techniques Chapter 5: R Programming Chapter 6: Machine Learning Paradigms Chapter 7: Different Machine Learning Models Chapter 8: Data Processing Chapter 9: Feature Engineering and Optimization Chapter 10: Evaluation and Validation of Results Chapter 11: Solutions Chapter 12: Data Set Bibliography