Microsoft Azure AI

1. Introduction to Azure AI
  • What is Azure AI?
  • Overview of Azure AI Platform and Services
  • Cognitive Services, Machine Learning, and Applied AI Use Cases
  • Navigating Azure Portal and Resource Management
2. Azure Cognitive Services
  • Overview of Azure Cognitive Services Categories
  • Using Pre-Built AI APIs via REST
  • Key Use Cases: Face Detection, Text Translation, Sentiment Analysis
3. Azure AI Vision Capabilities
  • Image Analysis using Computer Vision API
  • Face Detection and Recognition
  • Object Detection and Tagging
  • Custom Vision (Training Your Own Model)
4. Azure Language Capabilities
  • Text Analytics: Sentiment, Key Phrase Extraction, Entity Recognition
  • Language Understanding (LUIS)
  • QnA Maker and Azure AI Studio
  • Translator Service for Multi-language Applications
5. Azure Speech Capabilities
  • Speech-to-Text and Text-to-Speech
  • Speech Translation
  • Speaker Recognition and Custom Voice Models
  • Integrating Speech Capabilities into Apps
6. Azure Machine Learning Studio (Designer & Notebooks)
  • Creating Azure ML Workspaces
  • Low-Code ML Model Building with Azure ML Designer
  • Jupyter Notebooks in Azure ML
  • Training, Scoring, and Deploying ML Models
7. Responsible AI and Ethics in Azure
  • Fairness, Accountability, Transparency in AI
  • Interpret ML Models in Azure
  • Monitoring Bias and Drift
  • Responsible AI Dashboard
8. Building AI-Powered Bots with Azure
  • Overview of Azure Bot Services
  • Integrating QnA Maker with Bots
  • Bot Framework Composer
  • Deploying a Simple FAQ Chatbot
9. Deploying and Managing AI Solutions
  • Deploying Models to Azure Kubernetes Service (AKS)
  • Using Azure Functions for Model Serving
  • Setting Up CI/CD for AI with GitHub and Azure DevOps
  • Monitoring and Scaling AI Services
10. Capstone Project
  • Choose a Real-World Problem (e.g., Customer Support Bot, Image Classifier, Text Analyzer)
  • Design the Architecture using Azure AI Services
  • Implement, Deploy, and Monitor the Solution
  • Present Results with a Dashboard or Web Interface
What can you do?
  • Analyze images and videos
  • Understand and translate text
  • Convert speech to text and vice versa
  • Build and deploy ML models
  • Create smart chatbots
  • Use AI responsibly in your apps
Frequently Asked Questions
No, this course starts with low-code and no-code tools like Azure AI Studio and Cognitive Services. While basic knowledge of Azure and some coding (e.g., Python or C#) is helpful for advanced modules, it’s not required to get started.
You can build solutions like image analyzers, translation tools, chatbots, speech recognition systems, and even custom machine learning models—all using Azure's ready-to-use AI services.
Yes, the course covers how to train, evaluate, and deploy AI and ML models using Azure Machine Learning, as well as how to integrate them into real applications.