Applied Tiny Machine Learning (TinyML) for Scale edX - online learning platform

Overview

The Applied Tiny Machine Learning (TinyML) for Scale programme in collaboration with Harvard University - HarvardX is a Professional Certificate. 

Key Facts

Tiny Machine Learning (TinyML) is a cutting-edge field that brings the transformative power of machine learning (ML) to the performance- and power-constrained domain of tiny devices and embedded systems. Successful deployment in this field requires intimate knowledge of applications, algorithms, hardware, and software.

In this unique Professional Certificate program offered by Harvard University and Google ML, Data and AI Subject Matter experts, you will enhance your knowledge in the emerging field of TinyML, start applying the skills you have developed into real-world applications, and build the future possibilities of this transformative technology at scale.

In the first course of the program, Applications of TinyML, you will see how tools like voice recognition work in practice on small devices and you learn how common algorithms such as neural networks are implemented.

In Deploying TinyML, you will experience an open source hardware and prototyping platform to build your own tiny device. The program features projects based on an Arduino board (the TinyML Program Kit) and emphasizes hands-on experience with training and deploying machine learning into tiny embedded devices. The TinyML Program Kit has everything you need to unlock your imagination and build applications based on image recognition, audio processing, and gesture detection. Before you know it, you’ll be implementing an entire tiny machine learning application of your own design.

Job Outlook

• There are hundreds of billions of microcontrollers today, and an increasing desire to deploy machine learning models on these devices through TinyML. Learners who complete this program will be prepared to dive into this fast-growing field.
• Learners will have a fundamental understanding of TinyML applications and use cases and gain hands-on experience in programming with TensorFlow Micro and deploying TinyML models to an embedded microprocessor and system.
• Job postings in the United States requiring knowledge and skill working with Embedded Systems rose 71% in the last year according to Economic Modeling Systems Incorporated (2022).