Mastering Artificial Intelligence of Things (AIOT) (includes Edge AI and TinyML Workflows)

Artificial Intelligence of Things & Smart Systems
The next evolution of the Internet of Things is AIoT — Artificial Intelligence of Things, where connected devices do more than just collect data. They analyze, learn, and make intelligent decisions.
This course explores how Artificial Intelligence and Machine Learning can be integrated with IoT systems to create intelligent, autonomous, and efficient solutions.
Designed for engineers, developers, and technology professionals, this course provides a practical understanding of how AI models, edge computing, and embedded intelligence are transforming modern IoT systems.
You will learn how to build smart IoT systems capable of detecting patterns, predicting events, and making real-time decisions.
What Makes This Course Unique
- Focus on AI-driven IoT architectures and intelligent systems
- Covers Edge AI and TinyML for resource-constrained devices
- Explains how machine learning models work with IoT sensor data
- Real-world use cases including anomaly detection and predictive analytics
- Designed by a technology expert with 25+ years of industry experience
-Complements foundational IoT knowledge and moves into next-generation intelligent systems
What You Will Learn
In this course, you will explore how AI transforms traditional IoT systems into intelligent systems.
Key topics include:
- AIoT architecture and system design
- Machine learning concepts for IoT applications
- Working with IoT sensor data for AI models
- Edge AI for real-time intelligence on devices
- TinyML for running machine learning models on microcontrollers
- AI-based anomaly detection in IoT systems
- Predictive analytics for IoT data
- Designing intelligent IoT applications
Edge AI and TinyML
One of the key themes of this course is moving intelligence closer to devices.
Instead of sending all data to the cloud, modern IoT systems increasingly perform AI processing directly at the edge.
You will learn:
• How Edge AI reduces latency and improves real-time decision making
• How TinyML enables machine learning on microcontrollers
• How AI models can run on low-power IoT devices
Real-World AIoT Applications
AIoT is transforming industries across the globe.
In this course, we will explore real-world use cases such as:
• Predictive maintenance in industrial systems
• Smart cities and intelligent infrastructure
• Intelligent healthcare monitoring systems
• Smart energy management
• AI-driven anomaly detection in IoT networks
Who This Course Is For
This course is ideal for:
• IoT developers and engineers
• Software developers working with connected systems
• Data engineers and AI practitioners interested in IoT data
• Technology professionals exploring AI-driven IoT solutions
• Anyone who wants to understand the future of intelligent connected systems
Basic knowledge of IoT concepts or programming will be helpful.
Skills You Will Gain
By the end of this course, you will be able to:
- Understand the AIoT technology stack
- Apply machine learning techniques to IoT data
- Design AI-enabled IoT architectures
- Understand Edge AI and TinyML concepts
-Build intelligent IoT solutions for real-world applications
Why AIoT Matters
Traditional IoT systems collect massive amounts of data.
However, the real value comes from analyzing that data and turning it into actionable intelligence.
AIoT enables:
• smarter devices
• faster decision making
• more efficient systems
• predictive capabilities
This combination of AI and IoT is shaping the next generation of smart systems and intelligent infrastructure.
Start Your AIoT Journey
If you already understand IoT fundamentals and want to move to the next level of intelligent connected systems, this course will give you the knowledge and insights required to build AI-powered IoT solutions.
Join this course and start exploring the future of AI-driven smart systems.