Enroll Course: https://www.coursera.org/learn/introduction-to-embedded-machine-learning
In today’s rapidly evolving tech landscape, the ability to run sophisticated machine learning (ML) algorithms on low-power devices like microcontrollers is a game-changer. Coursera’s ‘Introduction to Embedded Machine Learning’ course dives headfirst into this exciting field, offering a comprehensive overview of how ML can be integrated into the embedded world.
This course is structured into three key modules, each building upon the last. The first module, ‘Introduction to Machine Learning,’ lays a solid foundation. It explains the fundamental concepts of ML, its applications in embedded systems, and introduces practical tools like Edge Impulse. You’ll learn to collect and process motion data, exploring feature extraction techniques such as RMS, Fourier transform, and PSD. This hands-on approach is crucial for understanding how raw data translates into actionable insights.
The second module, ‘Introduction to Neural Networks,’ delves into the architecture and training of neural networks. You’ll learn how to train these powerful models and deploy them for inference on embedded systems. The course uses a practical ‘magic wand’ demo, building on the motion data collected earlier, and then challenges you with a new motion classification project. This practical application solidifies your understanding of neural network concepts.
Finally, the ‘Audio Classification and Keyword Spotting’ module explores a specific, highly relevant application: audio processing on embedded devices. You’ll learn essential techniques like extracting Mel-Frequency Cepstral Coefficients (MFCCs) from audio data, training Convolutional Neural Networks (CNNs), and deploying these models onto microcontrollers. The module also touches upon implementation strategies and compares ML approaches with traditional sensor fusion techniques.
Overall, ‘Introduction to Embedded Machine Learning’ is an excellent course for anyone looking to enter the field of TinyML. It strikes a great balance between theoretical understanding and practical application, making complex topics accessible. The use of the Edge Impulse tool is particularly beneficial, providing a direct pathway to real-world embedded ML projects. I highly recommend this course to engineers, students, and hobbyists eager to explore the frontier of intelligent embedded systems.
Enroll Course: https://www.coursera.org/learn/introduction-to-embedded-machine-learning