IoT Machine Learning Applications in Telecom,Energy,and Agriculture: With Raspberry Pi and Arduino Using Python
by: Puneet Mathur
Print Length 页数: 296 pages
Publisher finelybook 出版社: Apress; 1st ed. edition (10 May 2020)
Language 语言: English
ISBN-10: 1484255488
ISBN-13: 9781484255483
Book Description
By finelybook
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
Front Matter
1. Getting Started: Necessary Software and Hardware
2. Overview of loT and lloT
3. Using Machine Learning with loT and lloT in Python
4. Using Machine Learning and the loT in Telecom,Energy,and Agriculture
5. Preparing for the Case Studies Implementation
6. Configuring the Energy Meter
7. Telecom Industry Case Study: Solving the Problem of Call Drops with the loT
8. Gantara power plant: Predictive Maintenance for an Industrial Machine
9. Agriculture Industry Case Study: Predicting a Cash Crop Yield
Back Matter
