AI-900: Microsoft Azure AI Fundamentals Exam Training Course

AI-900: Microsoft Azure AI Fundamentals

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123 Students Enrolled 85 Lectures 05:40:00 Hours

Curriculum For This Course

Introduction and basics on Azure

5Lectures

Time 00:29:00

Describe AI workloads and considerations

12Lectures

Time 00:15:00

Describe fundamental principles of machine learning on Azure

24Lectures

Time 02:13:00

Describe features of computer vision workloads on Azure

23Lectures

Time 01:41:00

Describe features of Natural Language Processing and Conversational AI workloads

20Lectures

Time 00:57:00

Exam Practice Section

1Lectures

Time 00:05:00

Introduction and basics on Azure
	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
	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
	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
	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
	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
	1. About the exam	5:00

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AI-900: Microsoft Azure AI Fundamentals

Curriculum For This Course

Free AI-900 Exam Questions & Microsoft AI-900 Dumps

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