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.
Contact us and Submit your application and get started by providing the necessary documents.
Our team will review your application and shortlist candidates based on skills and eligibility.
Attend hands-on training sessions in AI, Machine Learning, and Data Science to gain practical knowledge.
Work on real-world projects that solve critical industry problems, experiment with the code and sharpen your skills.
Receive your certification, showcasing your proficiency in AI and Data Science to potential employers.
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.
Program Modules
Introduction to Embedded AI
“Understand how AI integrates with embedded systems”
AI Algorithms for Embedded Systems
“Learn the algorithms powering AI in constrained environments”
Edge AI
“Bring intelligence closer to the device”
AI in Microcontrollers
“Understand the hardware behind AI applications”
Model Compression Techniques
“Minimize model size for embedded devices”
Quantization and Pruning
“Improve efficiency without losing accuracy”
AI Frameworks for Embedded Systems
“Explore TensorFlow Lite, Edge Impulse, and more”
Embedded AI Security
“Ensure security for AI models on edge devices”
AI-Enabled Sensor Networks
“Integrate AI for smarter data collection”
Computer Vision in Embedded AI
“Enable vision-based applications on low-power devices”
AI-Powered Automation
“Implement intelligent automation systems”
Capstone Project & Placement Assistance
“Apply your skills and get ready for placements”
COURSE OUTCOMES
Understand the fundamentals of embedded systems and AI integration.
Learn to design AI-based embedded solutions for real-time applications.
Seamlessly integrate sensors for real-time data collection & analysis.
Implement machine learning models on microcontrollers and edge devices.
Develop AI algorithms optimized for low-power embedded devices.
Collaborate on embedded AI projects using version control tools like Git.
Gain hands-on experience with tools like TensorFlow Lite and Edge Impulse.
Master techniques for data collection and processing in resource-constrained environments.
Career Opportunities
Open doors to a rewarding career :
- AI/ML Engineer
- Embedded Systems Engineer
- IoT Developer
- AI Researcher
- Data Scientist (Embedded AI)
- Firmware Developer (AI-Enabled Devices)
- Robotics Engineer
- Signal Processing Engineer
- AI Product Manager
- Edge Computing Engineer