Watch this video to learn information that will help you pass the AI-900: Course Azure AI Fundamentals with Virtual Simulations
Gain Certification: Microsoft Certified Azure AI Fundamentals

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INCLUDED EXAM OBJECTIVE CONTENT IN THIS VIDEO

Identify features of common AI workloads

*Understanding features of anomaly detection workloads
*Example of univariate anomaly detection
*Example of multivariate anomaly detection
*What is computer vision workloads?
*Conceptual usage of natural language processing workloads
*Visualizig knowledge mining principals

Identify common machine learning types

*Create an Azure Machine Learning workspace for machine learning scenarios
*What is regression machine learning?
*Building a pipeline with regression machine learning for cleaning a dataset
*Implement a regression machine learning scenario
*Evaluating the results of regression machine learning scenarios
*What is classification machine learning?
*Implement a classification machine learning scenario in Azure
*Understanding labels on a confusion matrix
*Clustering machine learning example

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Identify guiding principles for responsible AI

*Introduction to responsible AI
*Fairness and Inclusiveness in an AI solution
*Reliability and safety in an AI solution
*Privacy and security in an AI solution
*Transparency in an AI solution
*Accountability in an AI solution

Describe core machine learning concepts

*Understanding features and labels in a dataset for machine learning
*How training and validation datasets are used in machine learning

Describe capabilities of visual tools in Azure Machine Learning Studio

*Using Automated machine learning
*Understanding Azure Machine Learning Designer
*Cleaning up our existing Azure resources

Identify common types of computer vision solutions

*What are the Azure computer vision solutions?
*Creating an Azure computer vision resource
*Image classification and object detection solutions in vision studio
*Optical character recognition solutions in vision studio
*Facial detection and facial analysis solutions in vision studio
*Spatial analysis solutions in vision studio

Identify Azure tools and services for computer vision tasks

*Using the POSTMAN tool for interacting with Azure AI Services
*Implementing the capabilities of the Computer Vision service
*Implementing the capabilities of the Custom Vision service
*Implementing the capabilities of the Face service
*Implementing the capabilities of the Form Recognizer service

Identify features of common NLP Workload Scenarios

*What are the Azure AI Lanaguage features?
*Creating a language service resource in Azure
*Trying out key phrase extraction
*Trying out key entity recognition
*Trying out key sentiment analysis
*Trying out key language modeling
*Trying out key speech recognition and synthesis
*Trying out key translation

Identify Azure tools and services for NLP workloads

*Exploring the capabilities of the Language service
*Exploring the capabilities of the Speech service
*Exploring the capabilities of the Translator service
*Configuring Azure AI language to support questions and answers support

Identify considerations for conversational AI solutions on Azure

*Understanding the features and uses for bots
*Capabilities of Power Virtual Agents and the Azure Bot service
*Remove existing resource