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IoT machine learning applications in...

Mathur, Puneet.

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IoT machine learning applications in telecom, energy, and agriculture = with Raspberry Pi and Arduino Using Python /

Record Type:	Electronic resources : Monograph/item
Title/Author:	IoT machine learning applications in telecom, energy, and agriculture/ by Puneet Mathur.
Reminder of title:	with Raspberry Pi and Arduino Using Python /
Author:	Mathur, Puneet.
Published:	Berkeley, CA :Apress : : 2020.,
Description:	xv, 278 p. :ill., digital ;24 cm.
[NT 15003449]:	CHAPTER 1: Getting Started: Software and Hardware Needed -- CHAPTER 2: Overview of IoT and IIoT -- CHAPTER 3: Using Machine Learning with IoT and IIoT in Python -- CHAPTER 4: Using Machine Learning and IoT in Telecom, Energy, and Agriculture -- CHAPTER 5: Preparing for the Case Studies -- CHAPTER 6: Configuring IIoT Energy Meter -- CHAPTER 7: Telecom Industry Case Study: Solving the Problem of Call Drops with IoT -- CHAPTER 8: Energy Industry Case Study: Predictive Maintenance for an Industrial Machine -- CHAPTER 9: Agriculture Industry Case Study: Predicting a Cash Crop Yield.
Contained By:	Springer eBooks
Subject:	Internet of things. -
Online resource:	https://doi.org/10.1007/978-1-4842-5549-0
ISBN:	9781484255490

IoT machine learning applications in telecom, energy, and agriculture = with Raspberry Pi and Arduino Using Python /
Mathur, Puneet.

IoT machine learning applications in telecom, energy, and agriculturewith Raspberry Pi and Arduino Using Python /[electronic resource] :by Puneet Mathur. - Berkeley, CA :Apress :2020. - xv, 278 p. :ill., digital ;24 cm.

CHAPTER 1: Getting Started: Software and Hardware Needed -- CHAPTER 2: Overview of IoT and IIoT -- CHAPTER 3: Using Machine Learning with IoT and IIoT in Python -- CHAPTER 4: Using Machine Learning and IoT in Telecom, Energy, and Agriculture -- CHAPTER 5: Preparing for the Case Studies -- CHAPTER 6: Configuring IIoT Energy Meter -- CHAPTER 7: Telecom Industry Case Study: Solving the Problem of Call Drops with IoT -- CHAPTER 8: Energy Industry Case Study: Predictive Maintenance for an Industrial Machine -- CHAPTER 9: Agriculture Industry Case Study: Predicting a Cash Crop Yield.

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. You will: 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.

ISBN: 9781484255490

Standard No.: 10.1007/978-1-4842-5549-0doiSubjects--Topical Terms:

2057703
Internet of things.

LC Class. No.: TK5105.8857 / .M384 2020

Dewey Class. No.: 004.678

IoT machine learning applications in telecom, energy, and agriculture = with Raspberry Pi and Arduino Using Python /
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100 1 $a Mathur, Puneet. $3 3383993
245 1 0 $a IoT machine learning applications in telecom, energy, and agriculture $h [electronic resource] : $b with Raspberry Pi and Arduino Using Python / $c by Puneet Mathur.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2020.
300 $a xv, 278 p. : $b ill., digital ; $c 24 cm.
505 0 $a CHAPTER 1: Getting Started: Software and Hardware Needed -- CHAPTER 2: Overview of IoT and IIoT -- CHAPTER 3: Using Machine Learning with IoT and IIoT in Python -- CHAPTER 4: Using Machine Learning and IoT in Telecom, Energy, and Agriculture -- CHAPTER 5: Preparing for the Case Studies -- CHAPTER 6: Configuring IIoT Energy Meter -- CHAPTER 7: Telecom Industry Case Study: Solving the Problem of Call Drops with IoT -- CHAPTER 8: Energy Industry Case Study: Predictive Maintenance for an Industrial Machine -- CHAPTER 9: Agriculture Industry Case Study: Predicting a Cash Crop Yield.
520 $a 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. You will: 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.
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650 0 $a Machine learning. $3 533906
650 1 4 $a Machine Learning. $3 3382522
650 2 4 $a Python. $3 3201289
650 2 4 $a Open Source. $3 2210577
650 2 4 $a Hardware and Maker. $3 3134857
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856 4 0 $u https://doi.org/10.1007/978-1-4842-5549-0
950 $a Professional and Applied Computing (Springer-12059)