10 Free Certification Courses from Harvard University
3. Fundamentals of TinyML
Course description: Tiny Machine Learning (TinyML) is a rapidly growing area in the field of deep learning. It is becoming more accessible by the day. This course will provide you with a foundation to understand this emerging field. TinyML is at the intersection of embedded Machine Learning (ML) applications, algorithms, hardware, and software.
In contrast to mainstream machine learning (e.g., server and cloud), TinyML requires both software and embedded-hardware expertise. The first course in the TinyML Certificate series, Fundamentals of TinyML, will focus on the basics of machine learning, deep learning, and embedded devices and systems, such as smartphones and other tiny devices.
Throughout the course, you will learn data science techniques for collecting data and develop an understanding of learning algorithms to train basic machine learning models. At the end of this course, you will be able to understand the “language” behind TinyML and be ready to dive into the application of TinyML in future courses.
Following Fundamentals of TinyML, the other courses in the TinyML Professional Certificate program will allow you to see the code behind widely-used Tiny ML applications—such as tiny devices and smartphones—and deploy code to your own physical TinyML device.
Fundamentals of TinyML is an introductory course and not a prerequisite for Applications of TinyML or Deploying TinyML, but those with sufficient machine learning and embedded systems experience can skip this course and proceed to the more advanced courses. Course link
| DURATION | 5 weeks long |
| TIME COMMITMENT | 2 – 4 hours per week |
| PACE | Self-paced |
| SUBJECT | Computer Science |
| COURSE LANGUAGE | English |
| VIDEO TRANSCRIPT | English |
| DIFFICULTY | Introductory |
| CREDIT | Audit for Free Add a Verified Certificate for $299 |
| PLATFORM | edX |
What you’ll learn
- Fundamentals of Machine Learning (ML)
- Fundamentals of Deep Learning
- How to gather data for ML
- How to train and deploy ML models
- Understanding embedded ML
- Responsible AI Design