Guiding principles for Responsible AI | EDUCBA

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AI-900: Microsoft Azure AI Fundamentals Certification

Introduction

Introduction to Course (5:03)
What is Artificial Intelligence (6:09)
Machine Learning Model (10:23)
AI-900 Exam Requirements (5:31)

AI Workloads and Considerations

Common AI workloads (9:45)
Guiding principles for Responsible AI (6:53)
Privacy and Security (5:28)
Transparence and Accountability (5:53)
MS Official website for AI Principles (2:03)

Common ML Types and core ML Components

Common ML Types (7:12)
Dataset Features and Labels (4:28)
Training and Validation Dataset (7:55)
FPR and AUC (7:17)

No-Code ML

Auto ML (5:50)
Demo-Create Azure ML Workspace (6:49)
Use AutoML to Build a no-Code Model (7:26)
ML Designer (5:01)
ML Designer to Build a no-Code Model (14:38)

Computer Vision Workloads on Azure

Common types of Computer Vision Workloads (4:45)
Computer Vision Services in Microsoft Azure (8:10)
Using Computer Vision Service (6:10)
Using Custom Vision Service (7:17)

NLP on Azure

NLP Workloads (7:13)
NLP Services (5:08)

Conversational AI Workloads on Azure

Common Types of Conversational AI workloads (4:50)
Conversational AI Services in Azure (2:50)

Conclusion

Conclusion (0:48)

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Guiding principles for Responsible AI

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