Watch Course Overview
Course Curriculum
Introduction
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Introducing Background Control System
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Implementing Pub-Sub Communication Using MQTT
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- Introduction (0:58)
- Publish-Subscribe Communication Fundamentals (3:26)
- How MQTT Communication Protocol Works (2:26)
- Implementing MQTT Pub-Sub Communication For Demo Factory (3:13)
- Installing an MQTT Broker at the Edge (3:01)
- Creating Demo Factory MQTT Clients (4:31)
- Demonstrating MQTT Communication (3:45)
- Conclusion: setting The Stage For Machine Learning Integration (1:09)
Building & Integrating ML Image Classifier Into Control System
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- Introduction (1:55)
- Fundamentals of Convolutional Neural Networks and Tensorflow (3:36)
- Introduction to Google Colab ML Development Environment (1:40)
- Gathering Images and Creating Training Dataset (2:54)
- Training Image Classification Model Using Python, Tensorflow and Keras API (11:13)
- Saving and Testing Image Classification Model (3:58)
- Introducing Tensorflow Lite: ML For Edge Devices (3:26)
- Converting Image Classification Model Into Tensorflow Lite Model (6:39)
- Deploying Tensor-flow Lite ML Model onto Edge Device (1:34)
- Building Python App For Image Classification Using TFLite Model, raspberry Pi and Pi Camera (5:54)
- Integrating Python Image Classifier with Control System Using MQTT (5:17)
- Demonstrating Edge Intelligent Demo Factory (3:19)
Conclusion
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Hi,I'm Kudzai Manditereza
In this course, you'll learn how to:
Build an MQTT Pub-Sub Communication Network For a Control System.
Train and deploy a Machine Learning Image Classification model on Raspberry Pi using Tensorflow.
Integrate your ML Image Classification app into your control system using MQTT.
I'd love to have you on this exciting journey, you can enroll for this course below.