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AI Tactical Skills:  

IoT Ethical Hacker - Build Hack and Defend 

Who Should Attend:
  • Cyber Security engineers / analysts

  • Network and system administrators

  • Drone, & Robotic Engineers & Developers

  • Drone Operators

  • Digital Forensics Investigators

  • Penetration Testers

  • Cloud computing personnel

  • Cloud project managers

  • Operations support looking for career advancement

By the end of this course, participants will be able to:

  1. Understand the fundamentals of IoT and AI.

  2. Set up and configure development boards for AI-enabled IoT projects.

  3. Develop and deploy AI models for various IoT applications.

  4. Build and integrate IoT systems for smart homes, industrial applications, and smart cities.

  5. Analyze and visualize data from IoT devices using AI and cloud platforms.

  6. Implement a comprehensive AI-enabled IoT solution as a capstone project.

This course will provide a robust foundation for integrating AI with IoT, enabling learners to create innovative and intelligent projects across various domains.

Course Outline

Module 1: Introduction to AI and IoT

  • Basics of IOT / Artificial Intelligence

  • Introduction to AI concepts and its importance in IoT

  • Overview of Machine Learning (ML) and Deep Learning (DL)

  • Key AI frameworks and tools for IoT (TensorFlow, PyTorch, OpenCV)

 

Module 2: Setting Up the Development Environment

  • Introduction to IoT Development Platforms

  • Ai for IOT hardware device options

  • IoT Communication Protocols

  • Detailed look at MQTT, HTTP, CoAP, and other protocols

  • Setting up a basic MQTT server

  • Connecting sensors and actuators to the development board

Module 3: Handling Data

  • Delta Lake and Databricks

  • Data collection

  • Garbage data = no ML

  • Streaming data into IOT Hub​

  • Z-spike anomaly detection

 

Module 4: Machine Learning for IOT

  • IOT sensors with anomaly detection

  • Regression with IOMT

  • Classifying sensor with decision trees

  • Deep learning predictive maintenance

  • Face detection

Module 5: Deep Learning

  • Analyzing traffic patterns using AI

  • Keras fall detection

  • LSTM to predict device failure

  • Deploying models

Module 6: AI Anomaly Techniques for IoT

  • Z-Spikes using sense HAT on Rpi

  • Use of autoencoders in labeled data

  • Isolated forest for unlabeled data sets

  • Anomalies on the edge​

 

Module 7: Cloud Integration and Data Analytics

  • Integrating IoT with Cloud Platforms

  • Overview of cloud platforms (AWS IoT, Azure IoT, Google Cloud IoT)

  • Connecting IoT devices to the cloud

Module 8: Computer Vision

  • OpenCV camera deployment

  • Deep neural nets and caffe

  • Object detection with NVIDIA Jetson nano

  • PyTorch on GPU's

Module 9: NLP (natural language processing)

  • Speech to text

  • Luis (language understanding with Microsoft)

  • Deploying smart bots

  • Enhancing bots with QnA

Module 10: Optimization of MCU

  • ESP32 for IOT in Azure

  • Streaming machine learning with Kafka and Spark

  • Enriching data with Kafka

Module 11: Deploying to the edge

  • OTA updates

  • ​Offloading to the web with Tensorflow​.js

  • Mobile model

  • Distributed machine learning using Fog computing

Overall, AI significantly enhances the functionality, efficiency, and security of IoT systems, making it a critical component for the future of connected technologies.

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