EMBEDDED AI

The Embedded AI course blends embedded systems with artificial intelligence to create advanced, efficient solutions. Learn how to develop and implement AI algorithms on microcontrollers and edge devices, optimizing them for low-power environments. The course covers real-time data processing, machine learning integration, and smart device design. By the end, you'll be able to build intelligent systems for applications such as smart cities, autonomous vehicles, and healthcare.

📋

Apply for Internship

Contact us and Submit your application and get started by providing the necessary documents.

📝

Review & Selection

Our team will review your application and shortlist candidates based on skills and eligibility.

💻

Training Sessions

Attend hands-on training sessions in AI, Machine Learning, and Data Science to gain practical knowledge.

📊

Live Projects

Work on real-world projects that solve critical industry problems, experiment with the code and sharpen your skills.

🎓

Completion & Certification

Receive your certification, showcasing your proficiency in AI and Data Science to potential employers.

Course Outcomes

Embedded AI Course Outcomes

Master TinyML fundamentals and deploy machine learning models on resource-constrained embedded devices.

Optimize neural networks for edge computing using model quantization, pruning, and compression techniques.

Implement AI inference on microcontrollers using TensorFlow Lite Micro and OpenVINO frameworks.

Develop computer vision applications for embedded systems including object detection and image classification.

Build intelligent sensor fusion systems with real-time data processing and pattern recognition capabilities.

Create voice recognition and natural language processing applications for embedded AI devices.

Design predictive maintenance systems using AI-powered anomaly detection on industrial equipment.

Implement federated learning and on-device training techniques for privacy-preserving embedded AI solutions.

PREREQUISITE

Basic understanding of embedded systems.
Familiarity with programming languages like C or Python.
Knowledge of AI and machine learning fundamentals.
Experience with microcontrollers and sensors is a plus.

PREREQUISITE

Basic Programming Knowledge: Familiarity with programming languages like C or Python.
Understanding of Electronics: Basic knowledge of electronic components and circuits.
Familiarity with Microcontrollers: Basic understanding of microcontroller architecture and usage.
Networking Basics: Basic understanding of networking concepts, including IP addresses and protocols.
Willingness to Learn: A strong desire to learn about IoT technologies and applications.