The Resource IoT machine learning applications in telecom, energy, and agriculture : with Raspberry Pi and Arduino using Python, Puneet Mathur
IoT machine learning applications in telecom, energy, and agriculture : with Raspberry Pi and Arduino using Python, Puneet Mathur
Resource Information
The item IoT machine learning applications in telecom, energy, and agriculture : with Raspberry Pi and Arduino using Python, Puneet Mathur represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.This item is available to borrow from 2 library branches.
Resource Information
The item IoT machine learning applications in telecom, energy, and agriculture : with Raspberry Pi and Arduino using Python, Puneet Mathur represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.
This item is available to borrow from 2 library branches.
- 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
- Language
- eng
- Extent
- 1 online resource
- Note
- Includes index
- 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
- Isbn
- 9781484255490
- Label
- IoT machine learning applications in telecom, energy, and agriculture : with Raspberry Pi and Arduino using Python
- Title
- IoT machine learning applications in telecom, energy, and agriculture
- Title remainder
- with Raspberry Pi and Arduino using Python
- Statement of responsibility
- Puneet Mathur
- Subject
-
- Computer hardware
- Computer programming / software development
- Computers -- Hardware | General
- Computers -- Intelligence (AI) & Semantics
- Computers -- Programming Languages | Python
- Computers -- Programming | Open Source
- Electronic books
- Internet of things
- Machine learning
- Machine learning
- Machine learning
- Programming & scripting languages: general
- Internet of things
- Language
- eng
- 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
- Cataloging source
- YDX
- http://library.link/vocab/creatorName
- Mathur, Puneet
- Dewey number
- 004.67/8
- Index
- index present
- LC call number
- TK5105.8857
- Literary form
- non fiction
- Nature of contents
- dictionaries
- http://library.link/vocab/subjectName
-
- Internet of things
- Machine learning
- Programming & scripting languages: general
- Computer programming / software development
- Computer hardware
- Machine learning
- Computers
- Computers
- Computers
- Computers
- Internet of things
- Machine learning
- Label
- IoT machine learning applications in telecom, energy, and agriculture : with Raspberry Pi and Arduino using Python, Puneet Mathur
- Note
- Includes index
- Bibliography note
- Includes bibliographical references
- Carrier category
- online resource
- Carrier category code
-
- cr
- Carrier MARC source
- rdacarrier
- Content category
- text
- Content type code
-
- txt
- Content type MARC source
- rdacontent
- 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
- Control code
- 1154332931
- Dimensions
- unknown
- Extent
- 1 online resource
- Form of item
- online
- Isbn
- 9781484255490
- Media category
- computer
- Media MARC source
- rdamedia
- Media type code
-
- c
- Other control number
-
- 10.1007/978-1-4842-5
- 10.1007/978-1-4842-5549-0
- http://library.link/vocab/ext/overdrive/overdriveId
- com.springer.onix.9781484255490
- Specific material designation
- remote
- System control number
- (OCoLC)1154332931
- Label
- IoT machine learning applications in telecom, energy, and agriculture : with Raspberry Pi and Arduino using Python, Puneet Mathur
- Note
- Includes index
- Bibliography note
- Includes bibliographical references
- Carrier category
- online resource
- Carrier category code
-
- cr
- Carrier MARC source
- rdacarrier
- Content category
- text
- Content type code
-
- txt
- Content type MARC source
- rdacontent
- 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
- Control code
- 1154332931
- Dimensions
- unknown
- Extent
- 1 online resource
- Form of item
- online
- Isbn
- 9781484255490
- Media category
- computer
- Media MARC source
- rdamedia
- Media type code
-
- c
- Other control number
-
- 10.1007/978-1-4842-5
- 10.1007/978-1-4842-5549-0
- http://library.link/vocab/ext/overdrive/overdriveId
- com.springer.onix.9781484255490
- Specific material designation
- remote
- System control number
- (OCoLC)1154332931
Subject
- Computer hardware
- Computer programming / software development
- Computers -- Hardware | General
- Computers -- Intelligence (AI) & Semantics
- Computers -- Programming Languages | Python
- Computers -- Programming | Open Source
- Electronic books
- Internet of things
- Machine learning
- Machine learning
- Machine learning
- Programming & scripting languages: general
- Internet of things
Genre
Member of
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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.library.missouri.edu/portal/IoT-machine-learning-applications-in-telecom/7mhq_kJr1xE/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.missouri.edu/portal/IoT-machine-learning-applications-in-telecom/7mhq_kJr1xE/">IoT machine learning applications in telecom, energy, and agriculture : with Raspberry Pi and Arduino using Python, Puneet Mathur</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.missouri.edu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.missouri.edu/">University of Missouri Libraries</a></span></span></span></span></div>
