Integration of Internet Things and Machine Learning

The text outlines the integration of the Internet of Things and machine learning to drive technological advancements across various sectors. It further highlights common challenges and pitfalls encountered in the Internet of Things and machine learning projects. The book highlights the importance of using machine learning algorithms for smart intelligent devices, security and privacy issues of the Internet of Things. Features: Discusses comprehensively topics such as sensor data collection, data preprocessing, feature extraction, and model training Offers simplified explanations of algorithms like neural networks, decision trees, and clustering techniques, making them accessible to readers with limited technical background Showcases practical insights by presenting real-world applications of the Internet of Things, and machine learning in domains such as smart homes, healthcare, transportation, and manufacturing Covers issues such as data quality, model overfitting, security vulnerabilities, and scalability. Includes tutorials on setting up Internet of Things devices, collecting and preprocessing data, and building machine learning models The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.

Pages: 522, Edition: 1, Hardcover, CRC Press