🧩 Part 1/10

✔ What is Embedded Machine Learning?
✔ Why is Embedded Machine Learning Important?
✔ Benefits of Embedded Machine Learning
✔ Challenges of Embedded Machine Learning
✔ How Does Embedded Machine Learning Work?
✔ Use Cases of Embedded Machine Learning
✔ Smart Home Devices
✔ Wearable Health Monitors
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🧩 Part 2/10

✔ What is Embedded Machine Learning?
✔ Hardware Platforms for Embedded Machine Learning
✔ Microcontrollers
✔ Single-Board Computers
✔ Field-Programmable Gate Arrays (FPGAs)
✔ Application-Specific Integrated Circuits (ASICs)
✔ Comparison and Trade-offs of Hardware Platforms
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🧩 Part 3/10

✔ What is Embedded Machine Learning?
✔ Why Use a Framework for Embedded Machine Learning?
✔ Comparison of Popular Frameworks for Embedded Machine Learning
✔ TensorFlow Lite
✔ PyTorch Mobile
✔ Edge Impulse
✔ MicroPython
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🧩 Part 4/10

✔ Model Design Techniques
✔ Quantization
✔ Pruning
✔ Distillation
✔ Optimization
✔ Choosing the Right Hardware
✔ Understanding Constraints
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🧩 Part 5/10

✔ Data Collection
✔ Collecting Data from Embedded Devices
✔ Simulated Data Generation
✔ Data Preprocessing
✔ Data Augmentation Techniques
✔ Data Labeling
✔ Model Training
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🧩 Part 6/10

✔ Model Testing
✔ Accuracy Metrics
✔ Latency Evaluation
✔ Power Consumption Analysis
✔ Model Evaluation
✔ Trade-offs and Considerations
✔ Benchmarking
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🧩 Part 7/10

✔ Model Deployment Methods
✔ Over-the-Air (OTA) Updates
✔ Firmware Flashing
✔ Model Compression Techniques
✔ Quantization
✔ Pruning
✔ Model Encryption
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🧩 Part 8/10

✔ Model Monitoring
✔ Logging for Model Monitoring
✔ Profiling Techniques
✔ Model Debugging
✔ Visualizing Model Behavior
✔ Debugging Inference Errors
✔ Real-world Challenges
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🧩 Part 9/10

✔ Threats to Machine Learning Models on Embedded Devices
✔ Model Security
✔ Model Protection
✔ Authentication Mechanisms
✔ Role of Authentication in Model Security
✔ Implementing Secure Authentication
✔ Encryption Techniques for Model Protection
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🧩 Part 10/10

✔ Best Practices for Implementing Embedded Machine Learning
✔ Model Selection and Optimization
✔ Memory and Power Constraints
✔ Edge Device Deployment
✔ Tips for Efficient Embedded Machine Learning
✔ Quantization Techniques
✔ Pruning and Compression
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