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.

Microsoft Applied Skills

Microsoft Applied Skills are scenario-based credentials that provide learners with validation of targeted skills. These credentials are an efficient and trusted way to identify and deepen proficiency in scenario-based skillsets. The interactive training and validation enable learners to demonstrate proficiency by completing real-world tasks.

Applied Skills can help students prepare for the workforce by providing them with real-world problem-solving experience and validation of their skills.

This course is for:

• AI Engineers
• Developers
• Solution Architects

• Familiarity with Azure and the Azure portal
• Experience programming with C# or Python

After completing this course, students will be able to:

• Detect language from text
• Analyse text sentiment
• Extract key phrases, entities, and linked entities
• Understand question answering and how it compares to language understanding
• Create, test, publish and consume a knowledge base
• Implement multi-turn conversation and active learning
• Create a question answering bot to interact with using natural language
• Provision Azure resources for Azure AI Language resource
• Define intents, utterances, and entities
• Use patterns to differentiate similar utterances
• Use pre-built entity components
• Train, test, publish, and review an Azure AI Language model
• Understand types of classification projects
• Build a custom text classification project
• Tag data, train, and deploy a model
• Submit classification tasks from your own app
• Understand custom named entities and how they're labeled
• Build a Language service project
• Label data, train, and deploy an entity extraction model.
• Submit extraction tasks from your own app
• Provision a Translator resource
• Understand language detection, translation, and transliteration
• Specify translation options
• Define custom translations
• Provision an Azure resource for the Azure AI Speech service
• Use the Azure AI Speech to text API to implement speech recognition
• Use the Text to speech API to implement speech synthesis
• Configure audio format and voices
• Use Speech Synthesis Markup Language (SSML)
• Provision Azure resources for speech translation
• Generate text translation from speech
• Synthesise spoken translations

1 - Analyze text with Azure AI Language

• Understand question answering
• Compare question answering to Azure AI Language understanding
• Create a knowledge base
• Implement multi-turn conversation
• Test and publish a knowledge base
• Use a knowledge base
• Improve question answering performance

• Understand prebuilt capabilities of the Azure AI Language service
• Understand resources for building a conversational language understanding model
• Define intents, utterances, and entities
• Use patterns to differentiate similar utterances
• Use pre-built entity components
• Train, test, publish, and review a conversational language understanding model

• Understand types of classification projects
• Understand how to build text classification projects

• Understand custom named entity recognition
• Label your data
• Train and evaluate your model

• Provision an Azure AI Translator resource
• Understand language detection, translation, and transliteration
• Specify translation options
• Define custom translations

• Provision an Azure resource for speech
• Use the Azure AI Speech to Text API
• Use the text to speech API
• Configure audio format and voices
• Use Speech Synthesis Markup Language

• Provision an Azure resource for speech translation
• Translate speech to text
• Synthesize translations