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Course Outline
Introduction to TinyML and IoT
- What is TinyML?
- Benefits of TinyML in IoT applications
- Comparison of TinyML with traditional cloud-based AI
- Overview of TinyML tools: TensorFlow Lite, Edge Impulse
Setting Up the TinyML Environment
- Installing and configuring Arduino IDE
- Setting up Edge Impulse for TinyML model development
- Understanding microcontrollers for IoT (ESP32, Arduino, Raspberry Pi Pico)
- Connecting and testing hardware components
Developing Machine Learning Models for IoT
- Collecting and preprocessing IoT sensor data
- Building and training lightweight ML models
- Converting models to TensorFlow Lite format
- Optimizing models for memory and power constraints
Deploying AI Models on IoT Devices
- Flashing and running ML models on microcontrollers
- Validating model performance in real-world IoT scenarios
- Debugging and optimizing TinyML deployments
Implementing Predictive Maintenance with TinyML
- Using ML for equipment health monitoring
- Sensor-based anomaly detection techniques
- Deploying predictive maintenance models on IoT devices
Smart Sensors and Edge AI in IoT
- Enhancing IoT applications with TinyML-powered sensors
- Real-time event detection and classification
- Use cases: environmental monitoring, smart agriculture, industrial IoT
Security and Optimization in TinyML for IoT
- Data privacy and security in edge AI applications
- Techniques for reducing power consumption
- Future trends and advancements in TinyML for IoT
Summary and Next Steps
Requirements
- Experience with IoT or embedded systems development
- Familiarity with Python or C/C++ programming
- Basic understanding of machine learning concepts
- Knowledge of microcontroller hardware and peripherals
Audience
- IoT developers
- Embedded engineers
- AI practitioners
21 Hours
Testimonials (1)
The oral skills and human side of the trainer (Augustin).
