About the Course:

TinyML is revolutionizing the world of embedded systems by enabling machine learning (ML) on resource-constrained devices like microcontrollers and sensors. This training will cover the fundamental concepts behind TinyML, introduce key tools and frameworks, and demonstrate how to develop and deploy TinyML models for IoT applications. Attendees will gain hands-on experience in building efficient models for real-world use cases such as health monitoring, smart agriculture, and predictive maintenance.

Course Objective:

• Understand the key concepts and architecture of TinyML.
• Learn how to train machine learning models for embedded devices.
• Gain practical experience with tools and frameworks like TensorFlow Lite for Microcontrollers.
• Understand the trade-offs between accuracy, performance, and resource usage in TinyML.
• Be able to deploy TinyML models on various microcontroller platforms.
• Learn about real-world applications of TinyML in fields such as IoT, healthcare, and industrial automation.

Who is the Target Audience?

• Engineers and developers working on embedded systems and IoT solutions.
• Data scientists and machine learning practitioners interested in applying ML to edge devices.
• Hobbyists and enthusiasts looking to explore TinyML for personal projects.
• Academics or researchers in fields related to embedded computing and AI.

Basic Knowledge:

• Basic understanding of machine learning concepts (classification, regression, etc.).
• Familiarity with embedded systems or microcontrollers (e.g., Arduino, Raspberry Pi, or similar).
• Basic programming skills, ideally in Python or C/C++.