AI-900: Microsoft Azure AI Fundamentals
Get ready for your exam by enrolling in our comprehensive training course. This course includes a full set of instructional videos designed to equip you with in-depth knowledge essential for passing the certification exam with flying colors.
What’s included
- 85 : Lectures
- 5h 40m : Duration
$14.99/24.99
Introduction and basics on Azure
| Lectures |
|---|
| 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
| Lectures |
|---|
| 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
| Lectures |
|---|
| 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
| Lectures |
|---|
| 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
| Lectures |
|---|
| 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
| Lectures |
|---|
| 1. About the exam- 5m |
