Learn TinyML with online courses and programs
What is TinyML?
TinyML, short for Tiny Machine Learning, refers to the deployment of machine learning models on resource-constrained devices, such as microcontrollers and embedded systems. It combines the power of machine learning with the efficiency and low power consumption of microcontrollers, enabling intelligent decision making at the edge.
TinyML enables the development of intelligent devices that can operate autonomously without relying on constant connectivity to the cloud. This opens up a wide range of applications in areas such as healthcare, agriculture, smart homes, and wearable technology.
It also brings machine learning capabilities to devices with limited computational resources and promotes privacy and security by keeping data processing and analysis on-device, reducing the need for transmitting sensitive information to external servers.
Browse online TinyML courses New
TinyML course curriculum
An introductory TinyML course will likely cover foundational topics that provide learners with skills in machine learning, embedded systems, and developing intelligent applications. In addition to exploring the fundamentals of machine learning, deep learning, and embedded devices, you may cover:
Gathering data effectively for training models
Using Python to train and deploy TinyML models
Optimizing machine learning models for resource-constrained devices
Designing TinyML applications
Start building the knowledge you need to work in the TinyML field with edX. From accelerated boot camps to comprehensive programs that allow you to earn a bachelor’s degree or (for more advanced learners) a master’s degree, there are many different learning formats available to fit your needs. Busy professionals can even take advantage of executive education courses tailored to those in leadership and management positions. Find the right course for you.
Explore TinyML jobs
Learning TinyML can open up a range of exciting career opportunities at the intersection of machine learning, embedded systems, and Internet of Things (IoT). Some potential career paths for individuals skilled in TinyML include:
Embedded machine learning engineer: Develop and optimize machine learning models for deployment on resource-constrained devices. These professionals focus on designing efficient algorithms, implementing models on microcontrollers, and ensuring their optimal performance.
IoT device engineer: Develop intelligent and autonomous devices that can perform real-time data analysis and decision making at the edge. This may involve designing hardware architectures, integrating sensors, and implementing TinyML algorithms.
AI researcher: Develop novel algorithms, explore model compression and optimization techniques, and push the boundaries of efficient machine learning on resource-constrained devices.
Each of these roles will have different education and skills requirements. For example, you may be able to build relevant skills in a machine learning and AI MicroBootCamp™. However, some employers may seek candidates with a degree in computer science, depending on the role. Before deciding on a specific learning path, research the positions you hope to pursue and align your coursework with your career goals.