Artificial Intelligence · Course

AI for IoT

AI for IoT is a Advanced-level, 3 Weeks online program by NSTC. Master AI, IoT, Edge AI, Machine Learning, Deep Learning through hands-on projects, real datasets, and expert mentorship. Earn your e-Certification + e-Marksheet in AI for IoT.

ModeOnline (e-LMS)
LevelAdvanced
Duration3 Weeks

Attribute

Detail

Format

Online (e-LMS)

Level

Advanced

Duration

3 Weeks

Certification

e-Certification + e-Marksheet

₹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

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

AI for IoT

AI for IoT

About the AI for IoT Course

Program Highlights

Course Curriculum

Module 1: Introduction to AI and IoT

Module 2: IoT Data Processing and Analytics

Module 3: Applying AI to IoT Data

Module 4: AI Deployment in IoT Systems

Module 5: IoT Security and Ethical Considerations in AI

Tools, Techniques, or Platforms Covered

Real-World Applications

Who Should Attend & Prerequisites

Certification