What will you learn in Tiny Machine Learning (TinyML) course
- This Professional Certificate introduces the fundamentals of TinyML—deploying machine learning models on low-power embedded devices.
- Learners will understand how neural networks can run efficiently on microcontrollers and IoT systems.
- The program emphasizes signal processing, embedded programming, and model optimization techniques.
- Students will explore model quantization, compression, and performance trade-offs in constrained hardware environments.
- Hands-on labs demonstrate how to collect sensor data, train models, and deploy them to embedded systems.
- By completing the certificate, participants gain practical experience in building intelligent edge AI solutions.
Program Overview
Foundations of TinyML
4–6 Weeks
- Understand embedded systems basics.
- Learn fundamentals of neural networks.
- Explore constraints in memory and processing power.
- Study signal processing for sensor data.
Model Training and Optimization
4–6 Weeks
- Train machine learning models for embedded use.
- Apply quantization and model compression techniques.
- Evaluate latency and energy efficiency.
- Test models under real-time constraints.
Deployment on Microcontrollers
4–6 Weeks
- Deploy trained models to hardware devices.
- Integrate sensors and data pipelines.
- Debug embedded ML applications.
- Measure inference performance and reliability.
Capstone Project
Final Weeks
- Build an end-to-end TinyML system.
- Optimize deployment for scale.
- Demonstrate real-time embedded inference.
- Present a working edge AI application.
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Job Outlook
- TinyML and edge AI skills are increasingly valuable in IoT, robotics, smart devices, healthcare wearables, automotive systems, and industrial automation.
- Professionals trained in TinyML are sought for roles such as Embedded Systems Engineer, Edge AI Developer, IoT Solutions Engineer, and Machine Learning Engineer.
- Entry-level embedded AI professionals typically earn between $90K–$120K per year, while experienced edge AI engineers can earn $130K–$180K+ depending on specialization and region.
- As industries move toward on-device intelligence for privacy, latency, and cost efficiency, TinyML expertise continues to grow in demand.
- This certificate provides strong preparation for advanced AI hardware and embedded systems development careers.
