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Data Access and Distributed Processing for IoT

Data is everywhere: images, speech, text, weather information, the speed of your car, your last EMI, changing stock prices. With the integration of Internet of Things (IoT) systems, the amount of data produced has increased many-fold; an example is sensor readings, which could be taken for room temperature, soil alkalinity, and more. This data is stored and made available in various formats. In this chapter, we will learn how to read, save, and process data in some popular formats. Specifically, you will do the following:

Access data in TXT format
Read and write csv-formatted data via the CSV, pandas, and NumPy modules
Access JSON data using JSON and pandas
Learn to work with the HDF5 format using PyTables, pandas, and h5py
Handle SQL databases using SQLite and MySQL
Handle NoSQL using MongoDB
Work with Hadoop's Distributed...

Key benefits

Leverage the power of Python libraries such as TensorFlow and Keras to work with real-time IoT data

Process IoT data and predict outcomes in real time to build smart IoT models

Cover practical case studies on industrial IoT, smart cities, and home automation

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 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.

Who is this book 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.

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

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

$38.99

Hands-On Artificial Intelligence for Beginners

$54.99

Hands-On Artificial Intelligence for IoT

$54.99

Total $ 148.97

NSingh Feb 19, 2019

Must read book on Artificial Intelligence, machine learning and deep learning for IoT. It also cover Genetic Algorithms, Reinforcement learning and Generative Models for IoT.

Amazon Verified review

A. Jaokar Mar 02, 2019

This book was inspired by my work at Oxford UniversityI have known Amita from her previous book (co-authored with Antonio Gulli) but I have not contributed to this bookBased on my experience in teaching AI for IoT, it is not an easy subject due to its vast scope.This book first covers the Principles and then lays the groundwork in terms of tools(ex keras), datasets(ex air quality) and File formats (ex JSON)The book then cover Machine Learning in depth(linear regression, Logistic regression, SVMs, Naive Bayes, Decision trees, Ensemble learning ) followed by Deep Learning(Multi layer perceptron, CNN, RNN, Autoencoders) and then covers more complex algorithms(Genetic Algorithms, Reinforcement Learning and GANs). This is followed by Distributed AI(Spark) followed by application areas (Smart homes, Smart cities and Industrial IoT)The book then combines these ideas together in the last sectionOverall, it is a comprehensive and a hand-on approachWhat could be improved? More coverage on the Cloud and the Edge technologiesWhat’s the best feature? The hands-on (coding based) approachOverall, its very much recommended

RajA Apr 12, 2019

The book covers wide spectrum of AI topics from machine learning to deep learning, genetics algorithm, reinforcement learning, H2O etc to build smart AI models. Overall recommending and must read book.

Client d'Amazon Oct 06, 2019

très complet!

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

What do you get with Print?

Hands-On Artificial Intelligence for IoT

Data Access and Distributed Processing for IoT

TXT format

Using TXT files in Python

CSV format

XLSX format

Using OpenPyXl for XLSX files

Working with the JSON format

Using JSON files with the JSON module

HDF5 format

Using...

SQL data

The SQLite database engine

NoSQL data

TXT format

Using TXT files in Python

CSV format

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