Hands-On Artificial Intelligence for IoT: Expert machine learning and deep learning techniques for developing smarter IoT systems

IoT devices generate streams of data; the challenge is turning those streams into useful predictions and decisions. In Hands-On Artificial Intelligence for IoT, Amita Kapoor connects the foundations of connected-device systems with practical machine-learning and deep-learning methods for analyzing their data.

The book moves from IoT concepts and data handling to model-building techniques, using examples and tools that include Python, TensorFlow, and Keras. Its applied focus makes it relevant to readers who want to understand how AI methods can be brought to IoT problems—not just study the algorithms in isolation.

From IoT data to machine-learning models

Early chapters introduce IoT architectures, platforms, and the relationship between connected devices, big data, and AI. Kapoor then covers accessing and processing data from formats and sources such as CSV, JSON, spreadsheets, SQL and NoSQL databases, and HDFS. This groundwork helps frame the practical task of preparing distributed IoT data for analysis.

Explore core machine-learning methods

The book works through supervised learning approaches including linear and logistic regression, support vector machines, Naive Bayes, decision trees, and ensemble learning. Alongside model examples, it addresses topics such as feature scaling, overfitting, regularization, cross-validation, and hyperparameter tuning—considerations that matter when building and evaluating predictive systems.

Build toward deep learning and broader AI

Further chapters introduce neural networks and deep-learning architectures, with examples using TensorFlow and Keras. The book also extends beyond introductory models into other AI approaches and IoT applications, including time-series forecasting and case studies related to personal IoT, industrial IoT, and smart cities.

Who will find it useful?

This title is written for data science professionals and machine-learning developers interested in intelligent IoT systems. Readers who already have a basic grounding in machine-learning concepts will be better placed to follow its practical treatment of models, data workflows, and implementation tools.

For readers working where connected devices meet applied AI, Kapoor offers a structured route from IoT data fundamentals toward hands-on modeling and application.