Amita Kapoor 
Hands-On Artificial Intelligence for IoT [EPUB ebook] 
Expert machine learning and deep learning techniques for developing smarter IoT systems

Build smarter systems by combining artificial intelligence and the Internet of Things—two of the most talked about topics today

Key Features

• Leverage the power of Python libraries such as Tensor Flow and Keras to work with real-time Io T data
  
• Process Io T data and predict outcomes in real time to build smart Io T models
  
• Cover practical case studies on industrial Io T, smart cities, and home automation
  

Book Description

There are many applications that use data science and analytics to gain insights from terabytes of data. These apps, however, do not address the challenge of continually discovering patterns for Io T data. In Hands-On Artificial Intelligence for Io T, we cover various aspects of artificial intelligence (AI) and its implementation to make your Io T solutions smarter.

This book starts by covering the process of gathering and preprocessing Io T data gathered from distributed sources. You will learn different AI techniques such as machine learning, deep learning, reinforcement learning, and natural language processing to build smart Io T systems. You will also leverage the power of AI to handle real-time data coming from wearable devices. As you progress through the book, techniques for building models that work with different kinds of data generated and consumed by Io T devices such as time series, images, and audio will be covered. Useful case studies on four major application areas of Io T solutions are a key focal point of this book. In the concluding chapters, you will leverage the power of widely used Python libraries, Tensor Flow and Keras, to build different kinds of smart AI models.

By the end of this book, you will be able to build smart AI-powered Io T apps with confidence.

What you will learn

• Apply different AI techniques including machine learning and deep learning using Tensor Flow and Keras
  
• Access and process data from various distributed sources
  
• Perform supervised and unsupervised machine learning for Io T data
  
• Implement distributed processing of Io T 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 Io T, Industrial Io T, 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 Io T, Hands-On Artificial Intelligence for Io T 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.