IoT Machine Learning Applications in Telecom, Energy, and Agriculture

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.

• Machine Learning
• IoT
• Python
• Raspberry Pi
• Arduino Mega
• Cloud Computing
• Telecom

• Bangalore, India

  Puneet Mathur

Puneet Mathur is an author, AI consultant, and speaker who has over 20 years of corporate IT industry experience. He has risen from being a programmer to a third line manager working with multinationals such as HP, IBM, and Dell at various levels. For several years he has been working as an AI consultant through his company Boolbrite International for clients around the globe, by guiding and mentoring client teams stuck with AI and machine learning problems. He also conducts leadership and motivational workshops, and AI-based hands-on corporate workshops. His latest bestselling book, Machine Learning Applications using Python (Apress, 2018), is for machine learning professionals who want to advance their career by gaining experiential knowledge from an AI expert. His other books include The Predictive Program Manager, Prediction Secrets, and Good Money Bad Money.

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