AI for Edge & Robotics — deploying AI models on devices

Categories: AI/ML, CSE
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About Course

The AI for Edge & Robotics course bridges the gap between Artificial Intelligence and real-world hardware applications.
You’ll learn how to deploy Machine Learning and Deep Learning models on edge devices such as Raspberry Pi, NVIDIA Jetson, and Arduino-based systems — enabling smart, offline, and low-latency AI-powered robots.

This course blends theory, practical deployment, and robotics integration, teaching you how to optimize, convert, and run AI models in constrained environments.
By the end, you’ll be able to design and deploy real-time vision, speech, and sensor-based AI systems for autonomous robotics, IoT, and embedded AI applications.

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What Will You Learn?

  • Understand Edge AI and its role in modern robotics.
  • Set up development environments for edge devices (Raspberry Pi, Jetson Nano).
  • Convert and optimize ML/DL models for on-device inference.
  • Use TensorFlow Lite, ONNX, and OpenVINO for edge deployment.
  • Integrate AI with sensors, cameras, and microcontrollers.
  • Implement object detection, tracking, and gesture recognition on devices.
  • Build lightweight, energy-efficient AI systems.
  • Deploy robotics applications with real-time decision-making capabilities.

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