Microsoft Azure AI Fundamentals
Exam AI-900: Machine Learning (ML) and Artificial Intelligence
On-Demand / 5 hours 11 minutes
Learn about machine learning (ML), artificial intelligence (AI) with Microsoft MVP Tim Warner and prepare for the AI-900: Microsoft Azure AI Fundamentals exam.
Course Instructor: Tim Warner
Timothy Warner is a Microsoft Most Valuable Professional (MVP) in Cloud and Datacenter Management who is based in Nashville, TN. His professional specialties include Microsoft Azure, cross-platform PowerShell, and all things Windows Server-related.
@TechTrainerTim
Course Outline
Describe Artificial Intelligence workloads and considerations
Learn how to identify features of common AI workloads and principles for responsible AI:
- Identify prediction/forecasting workloads
- Identify features of anomaly detection workloads
- Identify computer vision workloads
- Identify natural language processing or knowledge mining workloads
- Identify conversational AI workloads
- Identify guiding principles for responsible AI
- Describe considerations for fairness in an AI solution
- describe considerations for reliability and safety in an AI solution
- Describe considerations for privacy and security in an AI solution
Describe fundamental principles of machine learning on Azure
Learn how to identify common machine learning types, core machine learning concepts, and identify core tasks in creating a machine learning solution:
- Identify regression machine learning scenarios
- Identify classification machine learning scenarios
- Identify clustering machine learning scenarios
- Identify features and labels in a dataset for machine learning
- Describe how training and validation datasets are used in machine learning
- Describe how machine learning algorithms are used for model training
- Describe common features of model training and evaluation
- Describe common features of model deployment and management
Describe features of computer vision workloads on Azure
Learn how to identify common types of computer vision solution and Azure tools and services for computer vision tasks.
- Identify features of image classification solutions
- Identify features of object detection solutions
- Identify features of semantic segmentation solutions
- Identify features of optical character recognition solutions
- Identify features of facial detection, facial recognition, and facial analysis solutions
- Intify capabilities of the Computer Vision service
- Identify capabilities of the Custom Vision service
- Identify capabilities of the Face service
Describe features of Natural Language Processing (NLP) workloads on Azure
Learn how to Identify features of common NLP Workload Scenarios and Azure tools and services for NLP workloads
- Identify features and uses for key phrase extraction
- Identify features and uses for entity recognition
- Identify features and uses for sentiment analysis
- Identify features and uses for language modeling
- Identify features and uses for speech recognition and synthesis
- Identify features and uses for translation
Describe features of conversational AI workloads on Azure
Identify common use cases for conversational AI and Azure services for conversational AI.
- Identify features and uses for key phrase extraction
- Identify features and uses for entity recognition
- Identify features and uses for sentiment analysis
- Identify features and uses for language modeling
- Identify features and uses for speech recognition and synthesis
- Identify features and uses for translation
- Identify features and uses for webchat bots
- Identify features and uses for telephone voice menus
- Identify features and uses for personal digital assistants
- Identify common characteristics of conversational AI solutions
- Identify capabilities of the QnA Maker service
- Identify capabilities of the Azure Bot service Bot Framework
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