What you will learn
Apply different AI techniques including machine learning and deep learning using TensorFlow and Keras
Access and process data from various distributed sources
Perform supervised and unsupervised machine learning for IoT data
Implement distributed processing of IoT data over Apache Spark using the MLLib and H2O.ai platforms
Forecast time-series data using deep learning methods
Implementing AI from case studies in Personal IoT, Industrial IoT, and Smart Cities
Gain unique insights from data obtained from wearable devices and smart devices
Who this book is for
If you are a data science professional or a machine learning developer looking to build smart systems for IoT, Hands-On Artificial Intelligence for IoT is for you. If you want to learn how popular artificial intelligence (AI) techniques can be used in the Internet of Things domain, this book will also be of benefit. A basic understanding of machine learning concepts will be required to get the best out of this book.

Table of Contents
Principles and Foundations of IoT and AI
Data Access and Distributed Processing for IoT
Machine Learning for IoT
Deep Learning for IoT
Genetic Algorithms for IoT
Reinforcement Learning for IoT
GAN for IoT
Distributed AI for IoT
Personal and Home and IoT
AI for Industrial IoT
AI for Smart Cities IoT
Combining It All Together