IoT Machine Learning Applications in Telecom, Energy, and Agriculture - With Raspberry Pi and Arduino Using Python (Paperback, 1st ed.)

Apply machine learning using the Internet of Things (IoT) in the agriculture, telecom, and energy domains with case studies. This book begins by covering how to set up the software and hardware components including the various sensors to implement the case studies in Python. The case study section starts with an examination of call drop with IoT in the telecoms industry, followed by a case study on energy audit and predictive maintenance for an industrial machine, and finally covers techniques to predict cash crop failure in agribusiness. The last section covers pitfalls to avoid while implementing machine learning and IoT in these domains. After reading this book, you will know how IoT and machine learning are used in the example domains and have practical case studies to use and extend. You will be able to create enterprise-scale applications using Raspberry Pi 3 B+ and Arduino Mega 2560 with Python. What You Will Learn Implement machine learning with IoT and solve problems in the telecom, agriculture, and energy sectors with Python Set up and use industrial-grade IoT products, such as Modbus RS485 protocol devices, in practical scenarios Develop solutions for commercial-grade IoT or IIoT projects Implement case studies in machine learning with IoT from scratch Who This Book Is For Raspberry Pi and Arduino enthusiasts and data science and machine learning professionals.

General

Imprint:	Apress
Country of origin:	United States
Release date:	May 2020
First published:	2020
Authors:	Puneet Mathur
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	278
Edition:	1st ed.
ISBN-13:	978-1-4842-5548-3
Categories:	Books > Computing & IT > Computer hardware & operating systems > General Books > Computing & IT > Computer programming > Programming languages > General Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Promotions
LSN:	1-4842-5548-8
Barcode:	9781484255483

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