Curriculum For This Course
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Introduction and basics on Azure
5 Lectures Time 00:29:00 -
Describe AI workloads and considerations
12 Lectures Time 00:15:00 -
Describe fundamental principles of machine learning on Azure
24 Lectures Time 02:13:00 -
Describe features of computer vision workloads on Azure
23 Lectures Time 01:41:00 -
Describe features of Natural Language Processing and Conversational AI workloads
20 Lectures Time 00:57:00 -
Exam Practice Section
1 Lecture Time 00:05:00
Video tutorials list
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Introduction and basics on Azure
Video Name Time 1. Introduction to Azure 5:00 2. The Azure Free Account 5:00 3. Concepts in Azure 4:00 4. Quick view of the Azure portal 4:00 5. Lab - An example of creating a resource in Azure 11:00 -
Describe AI workloads and considerations
Video Name Time 1. Machine Learning and Artificial Intelligence 2:00 2. Prediction and Forecasting workloads 1:00 3. Anomaly Detection Workloads 1:00 4. Natural Language Processing Workloads 2:00 5. Computer Vision Workloads 1:00 6. Conversational AI Workloads 1:00 7. Microsoft Guiding principles for response AI - Accountability 2:00 8. Microsoft Guiding principles for response AI - Reliability and Safety 1:00 9. Microsoft Guiding principles for response AI - Privacy and Security 1:00 10. Microsoft Guiding principles for response AI - Transparency 1:00 11. Microsoft Guiding principles for response AI - Inclusiveness 1:00 12. Microsoft Guiding principles for response AI - Fairness 1:00 -
Describe fundamental principles of machine learning on Azure
Video Name Time 1. Section Introduction 1:00 2. Why even consider Machine Learning? 4:00 3. The Machine Learning Model 9:00 4. The Machine Learning Algorithms 9:00 5. Different Machine Learning Algorithms 3:00 6. Machine Learning Techniques 4:00 7. Machine Learning Data - Features and Labels 5:00 8. Lab - Azure Machine Learning - Creating a workspace 6:00 9. Lab - Building a Classification Machine Learning Pipeline - Your Dataset 11:00 10. Lab - Building a Classification Machine Learning Pipeline - Splitting data 7:00 11. Optional - Lab - Creating an Azure Virtual Machine 9:00 12. Lab - Building a Classification Machine Learning Pipeline - Compute Target 6:00 13. Lab - Building a Classification Machine Learning Pipeline - Completion 6:00 14. Lab - Building a Classification Machine Learning Pipeline - Results 8:00 15. Recap on what's been done so far 2:00 16. Lab - Building a Classification Machine Learning Pipeline - Deployment 7:00 17. Lab - Installing the POSTMAN tool 4:00 18. Lab - Building a Classification Machine Learning Pipeline - Testing 6:00 19. Lab - Building a Regression Machine Learning Pipeline - Cleaning Data 9:00 20. Lab - Building a Regression Machine Learning Pipeline - Complete Pipeline 3:00 21. Lab - Building a Regression Machine Learning Pipeline - Results 3:00 22. Feature Engineering 3:00 23. Automated Machine Learning 6:00 24. Deleting your resources 2:00 -
Describe features of computer vision workloads on Azure
Video Name Time 1. Section Introduction 2:00 2. Azure Cognitive Services 1:00 3. Introduction to Azure Computer Vision solutions 3:00 4. A look at the Computer Vision service 5:00 5. Lab - Setting up Visual Studio 2019 4:00 6. Lab - Computer Vision - Basic Object Detection - Visual Studio 2019 12:00 7. Lab - Computer Vision - Restrictions example 2:00 8. Lab - Computer Vision - Object Bounding Coordinates - Visual Studio 2019 3:00 9. Lab - Computer Vision - Brand Image - Visual Studio 2019 2:00 10. Lab - Computer Vision - Via the POSTMAN tool 5:00 11. The benefits of the Cognitive services 2:00 12. Another example on Computer Vision - Bounding Coordinates 2:00 13. Lab - Computer Vision - Optical Character Recognition 5:00 14. Face API 2:00 15. Lab - Computer Vision - Analyzing a Face 3:00 16. A quick look at the Face service 3:00 17. Lab - Face API - Using Visual Studio 2019 6:00 18. Lab - Face API - Using POSTMAN tool 5:00 19. Lab - Face Verify API - Using POSTMAN tool 7:00 20. Lab - Face Find Similar API - Using POSTMAN tool 8:00 21. Lab - Custom Vision 9:00 22. A quick look at the Form Recognizer service 2:00 23. Lab - Form Recognizer 8:00 -
Describe features of Natural Language Processing and Conversational AI workloads
Video Name Time 1. Section Introduction 1:00 2. Natural Language Processing 3:00 3. A quick look at the Text Analytics 1:00 4. Lab - Text Analytics API - Key phrases 4:00 5. Lab - Text Analytics API - Language Detection 1:00 6. Lab - Text Analytics Service - Sentiment Analysis 1:00 7. Lab - Text Analytics Service - Entity Recognition 3:00 8. Lab - Translator Service 3:00 9. A quick look at the Speech Service 1:00 10. Lab - Speech Service - Speech to text 4:00 11. Lab - Speech Service - Text to speech 1:00 12. Language Understanding Intelligence Service 2:00 13. Lab - Working with LUIS - Using pre-built domains 8:00 14. Lab - Working with LUIS - Adding our own intents 4:00 15. Lab - Working with LUIS - Adding Entities 2:00 16. Lab - Working with LUIS - Publishing your model 2:00 17. QnA Maker service 2:00 18. Lab - QnA Maker service 9:00 19. Bot Framework 2:00 20. Example of Bot Framework in Azure 3:00 -
Exam Practice Section
Video Name Time 1. About the exam 5:00
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