Overview
Develop natural language processing solutions with Azure AI Services
Natural language processing (NLP) solutions use language models to interpret the semantic meaning of written or spoken language. You can use the Language Understanding service to build language models for your applications.
As of 13th June, the AI-102 Develop AI Solutions in Azure course has been updated and restructured by combining together these five 1-day AI courses into a single, comprehensive learning experience:
- AI-3016: Develop generative AI apps in Azure
- AI-3026: Develop AI Agents on Azure
- AI-3002: Develop AI information extraction solutions in Azure
- AI-3003: Build a natural language processing solution with Azure AI Services
- AI-3004: Build an Azure AI Vision solution with Azure AI services
This update provides learners with a more cohesive and in-depth journey through key AI concepts and applications.
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Skills Covered
- Deploy a language resource, and use prebuilt models
- Create a custom text classification solution
- Create a custom named entity recognition (NER) solution
Who Should Attend
- AI Engineer
- Developer
- Solution Architect
Course Curriculum
Prerequisites
Before starting this learning path, you should already have:
- Familiarity with Azure and the Azure portal.
- Experience programming with C# or Python. If you have no previous programming experience, we recommend you complete the Take your first steps with C# or Take your first steps with Python learning path before starting this one.
Course Modules
Exam & Certification
To earn this Microsoft Applied Skills credential, learners demonstrate the ability to create a natural language processing (NLP) solution by using Azure AI Language.
Candidates for this credential should have a solid understanding of creating and using various Azure NLP models through both Language Studio and in code, including custom models. They should also have experience programming in either Python or C#, be familiar with the Azure portal, and be comfortable provisioning Azure AI resources.
Tasks performed
- Deploy a language resource, and use prebuilt models
- Create a custom text classification solution
- Create a custom named entity recognition (NER) solution
