Machine learning can be integrated into an embedded system by first training a model on a powerful external computer using relevant data. Once trained, the optimized model is compressed and deployed to the embedded device. This allows the system to make real-time decisions based on new data it receives from sensors or inputs. Techniques like model quantization and pruning help reduce memory and power usage, making it suitable for low-resource hardware. Applications include voice recognition, predictive maintenance, and smart monitoring. To gain hands-on experience and deeper knowledge, consider enrolling in a machine learning course.
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