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  • Book Overview & Buying Hands-On Artificial Intelligence for IoT
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Hands-On Artificial Intelligence for IoT

Hands-On Artificial Intelligence for IoT

By : Amita Kapoor, Hector Duran Lopez-Velarde
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Hands-On Artificial Intelligence for IoT

Hands-On Artificial Intelligence for IoT

5 (4)
By: Amita Kapoor, Hector Duran Lopez-Velarde

Overview of this book

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 IoT data. In Hands-On Artificial Intelligence for IoT, we cover various aspects of artificial intelligence (AI) and its implementation to make your IoT solutions smarter. This book starts by covering the process of gathering and preprocessing IoT 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 IoT 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 IoT devices such as time series, images, and audio will be covered. Useful case studies on four major application areas of IoT solutions are a key focal point of this book. In the concluding chapters, you will leverage the power of widely used Python libraries, TensorFlow 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 IoT apps with confidence.
Table of Contents (14 chapters)
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Prediction using linear regression

Aaron, a friend of mine, is a little sloppy with money and is never able to estimate how much his monthly credit card bill will be. Can we do something to help him? Well, yes, linear regression can help us to predict a monthly credit card bill if we have sufficient data. Thanks to the digital economy, all of his monetary transactions for the last five years are available online. We extracted his monthly expenditure on groceries, stationery, and travel and his monthly income. Linear regression helped not only in predicting his monthly credit card bill, it also gave an insight into which factor was most responsible for his spending.

This was just one example; linear regression can be used in many similar tasks. In this section, we'll learn how we can perform linear regression on our data.

Linear regression is a supervised learning task. It...

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