Current

Intermediate

TinyML Specialization

Coursera · Harvard University · Updated March 2026

Platform rating

4.4/5

Champ 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

Course Curriculum

10 chapters

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

TinyML

Edge AI

Microcontrollers

Harvard

IoT

Inference

ShareCompare

Add to StackGo to Course

Alternatives to TinyML Specialization

Compare

Current

AI on Jetson: Building Real-Time AI Applications

NVIDIA Deep Learning Institute (DLI) · NVIDIA

4.7

8.7/10

8 hours self-paced

Build real-time AI applications on NVIDIA Jetson edge devices. Cover deployment, optimization, and computer vision at the edge.

Free

ViewAddCompare

Deploy ML models on Arm-based edge devices. Learn TensorFlow Lite, model optimization, and real-time inference on embedded systems.

Deploy machine learning on edge devices and microcontrollers. Learn computer vision, anomaly detection, and MLOps for embedded AI.

Learn the fundamentals of deploying AI on edge devices including model optimization, MLOps for edge, and IoT integration.