Introduction to Embedded Machine Learning

Go to Course: https://www.coursera.org/learn/introduction-to-embedded-machine-learning

Introduction

## Course Review: Introduction to Embedded Machine Learning on Coursera ### Overview In the realm of technological advancements, machine learning (ML) stands out as a transformative force across various industries. As we strive for smarter, more efficient devices, the field of embedded machine learning is gaining traction, allowing sophisticated algorithms to operate on low-power hardware. The course "Introduction to Embedded Machine Learning" on Coursera is an excellent opportunity for anyone looking to dive into this fascinating intersection of machine learning and hardware. This course offers an expansive look at the principles of machine learning while simultaneously addressing how these principles can be applied to embedded systems like microcontrollers and single-board computers. With recent advancements enabling deep learning to run on energy-efficient devices, this course positions itself at the forefront of modern technology. ### Course Syllabus Breakdown The course is structured into three comprehensive modules, each designed to build upon the last, ensuring learners develop a solid foundation in both theory and practical application. **1. Introduction to Machine Learning** In this preliminary module, learners are introduced to the fundamental concepts of machine learning. The instructor tackles essential topics such as the problem-solving capabilities of ML, its limitations, and its application on embedded systems. You’ll learn about techniques to create new computer interfaces while collecting motion data for a "magic wand" demonstration. Key statistical measures like root mean square (RMS), Fourier transforms, and power spectral density (PSD) will also be explored, laying the groundwork for understanding data manipulation necessary for machine learning. **2. Introduction to Neural Networks** The second module dives deeper into the specifics of neural networks, offering insights into their functioning and training methods. You will continue the hands-on experience of building a motion classification system using data collected from your devices, such as smartphones and Arduino boards. The practical challenge of creating a new motion classification project allows you to apply your knowledge and showcases the real-world applicability of concepts learned in this module. **3. Audio Classification and Keyword Spotting** The final module is an exciting exploration of audio classification. Here, you will learn how to extract features from audio data, specifically focusing on mel-frequency cepstral coefficients (MFCCs). You will train a convolutional neural network (CNN) and deploy this neural network on a microcontroller, providing valuable insights into implementation strategies on embedded systems. This module also acknowledges the relevance of sensor fusion in the context of machine learning. ### Recommendations I highly recommend this course for anyone interested in machine learning, especially those curious about its applications in embedded systems. Whether you’re a student, a professional looking to upskill, or a hobbyist eager to experiment with new technology, this course provides valuable insights and practical experiences that are essential in today’s tech landscape. **Pros:** - Well-structured modules that cater to both novices and those with some background in the field. - Emphasizes practical applications, providing hands-on experience that is critical in mastering embedded machine learning concepts. - The use of popular tools (like Edge Impulse) makes it relevant to industry standards and practices. **Cons:** - Some prior knowledge of programming, particularly in Python, may enhance the learning experience, particularly when dealing with neural networks and data handling. - The course heavily emphasizes practical projects which may require access to specific hardware (e.g., microcontrollers), which could be a barrier for some learners. ### Conclusion "Introduction to Embedded Machine Learning" on Coursera is an enriching course that successfully marries theoretical knowledge with practical application. With the rapid evolution of machine learning technologies, familiarity with embedded systems has become increasingly vital. Enrolling in this course will not only enhance your understanding, but it will also empower you to contribute innovatively in a technology-driven future. Don't miss the opportunity to elevate your skills and stay ahead in the rapidly evolving tech landscape!

Syllabus

Introduction to Machine Learning

In this module, we will introduce the concept of machine learning, how it can be used to solve problems, and its limitations. We will also cover how machine learning on embedded systems, such as single board computers and microcontrollers, can be effectively used to solve problems and create new types of computer interfaces. Then, we will introduce the Edge Impulse tool and collect motion data for a "magic wand" demo. Finally, we will examine the various features that can be calculated from this raw motion data, including root mean square (RMS), Fourier transform, and power spectral density (PSD).

In this module, we will look at how neural networks work, how to train them, and how to use them to perform inference in an embedded system. We will continue the previous demo of creating a motion classification system using motion data collected from a smartphone or Arduino board. Finally, we will challenge you with a new motion classification project where you will have the opportunity to implement the concepts learning in this module and the previous module.

In this module, we cover audio classification on embedded systems. Specifically, we will go over the basics of extracting mel-frequency cepstral coefficients (MFCCs) as features from recorded audio, training a convolutional neural network (CNN) and deploying that neural network to a microcontroller. Additionally, we dive into some of the implementation strategies on embedded systems and talk about how machine learning compares to sensor fusion.