Current
Intermediate
40% Off
TinyML Specialization
Coursera · Harvard University · Updated March 2026
Platform rating
4.4/5
AI Tutor Rating
8.2/10
Duration
3-4 months
Classes
120
Deploy machine learning on microcontrollers from Harvard University covering TinyML, edge inference, and ML model deployment on resource-constrained devices.
What you'll get
Deploy ML models on edge devices and microcontrollers
Optimize models for low-power inference
Prototype edge AI products from concept to deployment
Fit
Best for
Autonomy Engineers
Self-Driving Developers
IoT Engineers
Systems Engineers
Not ideal for
Learners seeking only entry-level overviews
Prerequisites & pricing
Prerequisites
Basic Python and ML knowledge
Pricing
Subscription
Certification
Certificate
Growth Leverage: Completing the TinyML Specialization opens pathways to roles like Edge AI Engineer, Machine Learning Engineer, or IoT Solutions Architect, enhancing career prospects in industries focused on smart devices and automation. This specialization may also lead to certification opportunities with organizations specializing in embedded systems.
Skills Value: Skills gained from this course are in high demand as companies seek to implement AI on edge devices; professionals can command salaries upwards of $100,000 annually. Employers value the ability to optimize and deploy machine learning models for low-power applications in sectors like healthcare, automotive, and consumer electronics.
TinyML
Edge AI
Microcontrollers
Harvard
IoT
Inference