AI-900: Microsoft Azure AI Fundamentals

AI-900: Microsoft Azure AI Fundamentals

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Introduction and basics on Azure

  • 1. Introduction to Azure
    5m
  • 2. The Azure Free Account
    5m
  • 3. Concepts in Azure
    4m
  • 4. Quick view of the Azure portal
    4m
  • 5. Lab - An example of creating a resource in Azure
    11m

Describe AI workloads and considerations

  • 1. Machine Learning and Artificial Intelligence
    2m
  • 2. Prediction and Forecasting workloads
    1m
  • 3. Anomaly Detection Workloads
    1m
  • 4. Natural Language Processing Workloads
    2m
  • 5. Computer Vision Workloads
    1m
  • 6. Conversational AI Workloads
    1m
  • 7. Microsoft Guiding principles for response AI - Accountability
    2m
  • 8. Microsoft Guiding principles for response AI - Reliability and Safety
    1m
  • 9. Microsoft Guiding principles for response AI - Privacy and Security
    1m
  • 10. Microsoft Guiding principles for response AI - Transparency
    1m
  • 11. Microsoft Guiding principles for response AI - Inclusiveness
    1m
  • 12. Microsoft Guiding principles for response AI - Fairness
    1m

Describe fundamental principles of machine learning on Azure

  • 1. Section Introduction
    1m
  • 2. Why even consider Machine Learning?
    4m
  • 3. The Machine Learning Model
    9m
  • 4. The Machine Learning Algorithms
    9m
  • 5. Different Machine Learning Algorithms
    3m
  • 6. Machine Learning Techniques
    4m
  • 7. Machine Learning Data - Features and Labels
    5m
  • 8. Lab - Azure Machine Learning - Creating a workspace
    6m
  • 9. Lab - Building a Classification Machine Learning Pipeline - Your Dataset
    11m
  • 10. Lab - Building a Classification Machine Learning Pipeline - Splitting data
    7m
  • 11. Optional - Lab - Creating an Azure Virtual Machine
    9m
  • 12. Lab - Building a Classification Machine Learning Pipeline - Compute Target
    6m
  • 13. Lab - Building a Classification Machine Learning Pipeline - Completion
    6m
  • 14. Lab - Building a Classification Machine Learning Pipeline - Results
    8m
  • 15. Recap on what's been done so far
    2m
  • 16. Lab - Building a Classification Machine Learning Pipeline - Deployment
    7m
  • 17. Lab - Installing the POSTMAN tool
    4m
  • 18. Lab - Building a Classification Machine Learning Pipeline - Testing
    6m
  • 19. Lab - Building a Regression Machine Learning Pipeline - Cleaning Data
    9m
  • 20. Lab - Building a Regression Machine Learning Pipeline - Complete Pipeline
    3m
  • 21. Lab - Building a Regression Machine Learning Pipeline - Results
    3m
  • 22. Feature Engineering
    3m
  • 23. Automated Machine Learning
    6m
  • 24. Deleting your resources
    2m

Describe features of computer vision workloads on Azure

  • 1. Section Introduction
    2m
  • 2. Azure Cognitive Services
    1m
  • 3. Introduction to Azure Computer Vision solutions
    3m
  • 4. A look at the Computer Vision service
    5m
  • 5. Lab - Setting up Visual Studio 2019
    4m
  • 6. Lab - Computer Vision - Basic Object Detection - Visual Studio 2019
    12m
  • 7. Lab - Computer Vision - Restrictions example
    2m
  • 8. Lab - Computer Vision - Object Bounding Coordinates - Visual Studio 2019
    3m
  • 9. Lab - Computer Vision - Brand Image - Visual Studio 2019
    2m
  • 10. Lab - Computer Vision - Via the POSTMAN tool
    5m
  • 11. The benefits of the Cognitive services
    2m
  • 12. Another example on Computer Vision - Bounding Coordinates
    2m
  • 13. Lab - Computer Vision - Optical Character Recognition
    5m
  • 14. Face API
    2m
  • 15. Lab - Computer Vision - Analyzing a Face
    3m
  • 16. A quick look at the Face service
    3m
  • 17. Lab - Face API - Using Visual Studio 2019
    6m
  • 18. Lab - Face API - Using POSTMAN tool
    5m
  • 19. Lab - Face Verify API - Using POSTMAN tool
    7m
  • 20. Lab - Face Find Similar API - Using POSTMAN tool
    8m
  • 21. Lab - Custom Vision
    9m
  • 22. A quick look at the Form Recognizer service
    2m
  • 23. Lab - Form Recognizer
    8m

Describe features of Natural Language Processing and Conversational AI workloads

  • 1. Section Introduction
    1m
  • 2. Natural Language Processing
    3m
  • 3. A quick look at the Text Analytics
    1m
  • 4. Lab - Text Analytics API - Key phrases
    4m
  • 5. Lab - Text Analytics API - Language Detection
    1m
  • 6. Lab - Text Analytics Service - Sentiment Analysis
    1m
  • 7. Lab - Text Analytics Service - Entity Recognition
    3m
  • 8. Lab - Translator Service
    3m
  • 9. A quick look at the Speech Service
    1m
  • 10. Lab - Speech Service - Speech to text
    4m
  • 11. Lab - Speech Service - Text to speech
    1m
  • 12. Language Understanding Intelligence Service
    2m
  • 13. Lab - Working with LUIS - Using pre-built domains
    8m
  • 14. Lab - Working with LUIS - Adding our own intents
    4m
  • 15. Lab - Working with LUIS - Adding Entities
    2m
  • 16. Lab - Working with LUIS - Publishing your model
    2m
  • 17. QnA Maker service
    2m
  • 18. Lab - QnA Maker service
    9m
  • 19. Bot Framework
    2m
  • 20. Example of Bot Framework in Azure
    3m

Exam Practice Section

  • 1. About the exam
    5m