IoT Machine Learning Applications in Telecom, Energy, and Agriculture : With Raspberry Pi and Arduino Using Python / by Puneet Mathur.

Format:

Book

Author/Creator:

Mathur, Puneet, Author.

Language:

English

Subjects (All):

Machine learning.

Python (Computer program language).

Open source software.

Computer programming.

Computer input-output equipment.

Machine Learning.

Python.

Open Source.

Hardware and Maker.

Local Subjects:

Machine Learning.

Python.

Open Source.

Hardware and Maker.

Physical Description:

1 online resource (XV, 278 p. 105 illus.)

Edition:

1st ed. 2020.

Other Title:

Internet of Things machine learning applications in telecom, energy, and agriculture

Place of Publication:

Berkeley, CA : Apress : Imprint: Apress, 2020.

System Details:

text file

Summary:

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.

Contents:

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. .

Notes:

Includes index.

Includes bibliographical references.

OCLC:

1198933510