An IoT & Artificial Intelligence (AI) course from zero to hero should cover the fundamentals to advanced concepts, practical applications, and real-world projects. Here a complete roadmap:

1: Introduction to IoT & AI

✅ What is IoT? History & Evolution
✅ What is AI? History & Evolution
✅ Differences & Relationship Between IoT and AI
✅ Applications of IoT & AI in Real Life

2: IoT Fundamentals

✅ IoT Architecture (Perception, Network, and Application Layers)
✅ IoT Communication Protocols (MQTT, HTTP, CoAP, AMQP)
✅ IoT Hardware (Raspberry Pi, Arduino, ESP8266, ESP32)
✅ Sensors & Actuators (Types & Uses)
✅ IoT Cloud Platforms (AWS IoT, Google IoT Core, Microsoft Azure IoT, ThingSpeak)

3: Networking & IoT Communication

✅ Basics of Networking (TCP/IP, Wi-Fi, Bluetooth, Zigbee, LoRaWAN, NB-IoT)
✅ Data Transmission in IoT (JSON, REST APIs, WebSockets)
✅ Hands-on: Connecting Sensors to Cloud Platforms
✅ Security in IoT (Encryption, Authentication, Firewalls)

4: Programming for IoT (Python & Embedded C)

✅ Introduction to Python & Embedded C for IoT
✅ Working with Raspberry Pi & Arduino
✅ GPIO Programming (LED, Sensors, Motors)
✅ IoT Data Logging using MySQL & Firebase
✅ Hands-on: Sending Sensor Data to Cloud

5: Introduction to Artificial Intelligence

✅ What is AI? Types of AI (Weak AI, Strong AI, AGI)
✅ Machine Learning vs Deep Learning vs AI
✅ AI Algorithms Overview (Supervised, Unsupervised, Reinforcement Learning)
✅ Python Libraries for AI (NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, PyTorch)

6: Machine Learning for IoT

✅ Data Collection & Preprocessing from IoT Devices
✅ Feature Engineering for IoT Data
✅ Classification & Regression Models (Decision Trees, Random Forest, SVM)
✅ Hands-on: Predictive Maintenance using ML in IoT
✅ Deploying ML Models on Edge Devices (TinyML, TensorFlow Lite)

7: Deep Learning for IoT

✅ Introduction to Neural Networks (Perceptron, CNN, RNN, LSTMs)
✅ Object Detection & Image Processing for IoT (OpenCV, YOLO, TensorFlow)
✅ NLP in IoT (Voice Commands, Chatbots, Google Assistant Integration)
✅ Hands-on: AI-Based Anomaly Detection in IoT Data

8: Edge AI & Edge Computing in IoT

✅ What is Edge AI? Benefits & Challenges
✅ Deploying AI Models on Edge Devices (Raspberry Pi, Jetson Nano)
✅ Real-time AI on IoT Devices (Voice Recognition, Image Processing)
✅ Hands-on: Smart Home Automation using Edge AI

9: IoT & AI in Cloud Computing

✅ Cloud-Based AI Services (AWS AI, Google AI, Azure AI)
✅ IoT Data Processing in Cloud
✅ AutoML for IoT Applications
✅ Hands-on: Building AI-powered IoT Dashboard with Cloud Analytics

10: Cybersecurity & Ethical Considerations

✅ IoT Security Risks & Best Practices
✅ AI in Cybersecurity (Intrusion Detection, Threat Analysis)
✅ Ethics of AI & IoT (Bias, Privacy, GDPR, Data Protection)
✅ Case Studies on AI & IoT Security Attacks

11: IoT & AI Projects (Hands-on Practice)

✅ Smart Home Automation with AI (Voice-Controlled IoT Devices)
✅ AI-Based Predictive Maintenance for Industrial IoT (Detect Equipment Failures)
✅ Face Recognition Smart Door Lock using AI & IoT
✅ AI-Powered Health Monitoring System (IoT Wearables & ML Predictions)
✅ Smart Traffic System using IoT & AI (Vehicle Detection, Smart Lights)
✅ AI-Based Chatbot for IoT Device Control

12: Final Capstone Project & Career Guidance

✅ Selecting a Real-World AI & IoT Project
✅ End-to-End Implementation & Deployment
✅ Resume Building & Career Paths in AI & IoT
✅ Job Opportunities & Certifications (AWS IoT, Google AI, IBM AI, Azure AI)