AI-3003: Build a natural language processing solution with Azure AI Services

Course AI-3003: Build a natural language processing solution with Azure AI Services

To earn this Microsoft Applied Skills credential, students demonstrate the ability to create a natural language processing (NLP) solution using Azure AI Language .

Candidates for this credential must have a solid understanding of creating and using various Azure NLP models through Language Studio and in code, including custom models. They should also have experience programming in Python or C# , be familiar with the Azure portal , and be comfortable provisioning Azure AI resources.

Level: Intermediate - Role: AI Engineer, Developer, Solutions Architect - Product: Azure AI Language - Subject: Artificial Intelligence

Course aimed at

This course is primarily aimed at application developers who want to incorporate natural language processing into their applications. Students should have experience programming in Python or C#, be familiar with the Azure Portal, and be comfortable with Azure AI resource provisioning.

Objectives of the AI-3003 training

In this course, you will learn how to implement natural language processing (NLP) solutions using Microsoft Azure AI services. Specifically, you will learn how to do the following:

• Design a solution that processes natural language using Azure AI Language.
  
• Create a solution that analyzes text using preconfigured features.
  
• Train a model for a custom language solution for question answering and conversational language recognition.
  
• Describe, synthesize, and translate speech
  

AI-3003 Course Content

Module 1 Text Analysis with Azure AI Language

• Provision an Azure AI Language resource
• Detect language
• Key phrase extraction
• Opinion analysis
• Extract entities
• Extraction of related entities
• Exercise: Text Analysis

Module 2: Creating Question Answer Solutions with Azure AI Language

• Understanding the answer to questions
  
• Comparison of responses to questions with Azure AI Language Understanding
  
• Creating a knowledge base
  
• Implement a multi-turn conversation
  
• Testing and publishing a knowledge base
  
• Use of a knowledge base
  
• Improved question response performance
  
• Exercise: Creating a solution to answer questions

Module 3: Creating a model for recognizing conversational language

• Recognition of the integrated functionalities of the Azure AI Language service
• Description of the resources for creating a conversational language recognition model
• Definition of intentions, expressions, and entities
• Using patterns to differentiate similar expressions
• Use of pre-built entity components
• Training, testing, publication and review of a conversational language recognition model
• Exercise: Creating a conversational language recognition model for Azure AI Services

Module 4 Creating a custom text classification project

• Introduction
• Description of classification project types
• Description of how to compile text classification projects
• Exercise: Text classification

Module 5 Recognition of entities with custom names

• Description of the recognition of entities with custom names
  
• Data labeling
  
• Model training and evaluation
  
• Exercise: Extraction of custom entities

Module 6 Text translation with the Azure AI Translator service

• Provisioning an Azure AI Translator resource
  
• Description of language detection, translation, and transliteration
  
• Specify translation options
  
• Definition of custom translations
  
• Exercise: Text translation using the Azure AI Translator service
  

Module 7: Creating voice-enabled applications with Azure AI Services

• Provisioning an Azure resource for voice
  
• Using the Azure AI Speech-to-Text API
  
• Using the Text-to-Speech API
  
• Audio format and voice settings
  
• Use of Speech Synthesis Markup Language
  
• Exercise: Creating a voice-enabled application

Module 8 Voice translation with the Azure AI voice service

• Provisioning an Azure resource for voice translation
  
• Voice-to-text translation
  
• Summary of translations
  
• Exercise: Voice translation

Module 9 Development of an audio-enabled generative AI application

• Implementation of a multimodal model
  
• Development of an audio-based chat application
  
• Exercise: Developing an audio-enabled chat application

Prerequisites

Before starting this learning path, you must have the following:

• Familiarity with Azure and the Azure Portal
• Experience in C# programming in Python. If you have no prior programming experience, it is recommended that you complete the Getting Started with C# or Getting Started with Python learning path before starting this one.

Language

• Course: English / Spanish
• Labs: English / Spanish

Microsoft Applied Skills

This course is part of the Microsoft Applied Skills Credentials.

To earn this Microsoft Applied Skills credential, students demonstrate the ability to create a natural language processing (NLP) solution using Azure AI Language.

Applied Skills: Explore all credentials in one guide