| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Advanced |
| Duration | 3 Weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹4249 / $56 |
| Tools | TensorFlow TensorFlow Lite PyTorch Scikit-Learn Raspberry Pi Arduino MQTT CoAP AWS IoT Azure IoT Hub |
About the AI for IoT Course
The AI for IoT: Intelligent Integration of AI with the Internet of Things is a 3‑week intensive program that blends artificial intelligence with IoT.
Through lectures, labs, and real‑world case studies in healthcare and urban planning, participants will design, deploy, and optimise AI‑driven IoT solutions and leave with a certificate of completion.
Program Highlights
• Comprehensive coverage of AI for IoT from fundamentals to advanced applications
• Hands-on projects and real-world case studies in Artificial Intelligence
• Expert-curated curriculum aligned with current industry standards
• Access to recorded lectures and e-LMS platform for flexible, self-paced learning
• e-Certification and e-Marksheet upon successful completion
• Dedicated mentor support and interactive doubt-clearing sessions
• Practical experience with tools: TensorFlow, TensorFlow Lite, PyTorch, Scikit-Learn
• Career-oriented training for academic and professional growth in Artificial Intelligence
Course Curriculum
Module 1: Introduction to AI and IoT
- Explore core concepts of IoT ecosystems and AI techniques
- Analyse the synergy between AI and IoT across smart cities, wearables and autonomous vehicles
- Set up hardware (Raspberry Pi, Arduino) and software tools (TensorFlow, MQTT)
Module 2: IoT Data Processing and Analytics
- Collect and stream sensor data to cloud and edge platforms
- Preprocess data – cleaning, normalising, encoding for AI models
- Implement edge‑AI on Raspberry Pi and cloud‑AI on AWS/Google/Azure IoT
Module 3: Applying AI to IoT Data
- Build supervised and unsupervised models for predictive maintenance and anomaly detection
- Deploy deep‑learning models (RNN/LSTM, CNN) for time‑series forecasting and computer‑vision IoT applications
- Create reinforcement‑learning agents for smart‑grid optimisation
Module 4: AI Deployment in IoT Systems
- Optimise and deploy models on edge devices using TensorFlow Lite
- Integrate AI with cloud IoT platforms (Azure IoT Hub, AWS IoT Core) for scalable solutions
- Apply security best practices for cloud‑AI‑IoT deployments
Module 5: IoT Security and Ethical Considerations in AI
- Identify IoT threat vectors and use AI for intrusion detection
- Address data‑privacy, bias, and transparent AI model design
- Implement ethical guidelines for responsible AI‑IoT solutions
Tools, Techniques, or Platforms Covered
TensorFlow TensorFlow Lite PyTorch Scikit-Learn Raspberry Pi Arduino MQTT CoAP AWS IoT Azure IoT Hub
Real-World Applications
- Apply AI for IoT skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical Artificial Intelligence competencies
- Solve industry-relevant problems using AI for IoT methodologies and tools
- Contribute to open-source projects and collaborative research in Artificial Intelligence
- Prepare for competitive examinations, interviews, and professional certifications in Artificial Intelligence
Who Should Attend & Prerequisites
- Industry‑recognised e‑Certification + e‑Marksheet from NSTC
- Hands‑on training with practical projects and industrial datasets
- Dedicated expert mentorship and doubt resolution
Certification
