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 should 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 programming experience 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 programming experience in Python or C#, be familiar with the Azure portal, and be comfortable provisioning Azure AI resources.

AI-3003 Training Objectives

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

• Design a solution that processes natural language with 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 voice
  

AI-3003 Course Content

Module 1 Text Analysis with Azure AI Language

• Provision an Azure AI Language resource
• Detect language
• Keyphrase Extraction
• Opinion analysis
• Extract entities
• Extraction of linked entities
• Exercise: Text Analysis

Module 2: Creating Question-Answering Solutions with Azure AI Language

• Understanding the answer to questions
  
• Comparing Question Responses with Azure AI Language Understanding
  
• Creating a knowledge base
  
• Implement a multi-turn conversation
  
• Testing and publishing a knowledge base
  
• Using a knowledge base
  
• Improved question answering performance
  
• Exercise: Creating a Question-Answering Solution

Module 3 Creating a Conversational Language Recognition Model

• Recognizing the built-in capabilities of the Azure AI Language service
• Description of resources for creating a conversational language recognition model
• Definition of intentions, expressions and entities
• Using patterns to differentiate similar expressions
• Using pre-built entity components
• Training, testing, publishing, and reviewing a conversational language recognition model
• Exercise: Creating an Azure AI Services Conversational Language Recognition Model

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 Recognizing Entities with Custom Names

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

Module 6: Translating Text 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: Translating text with the Azure AI Translator service
  

Module 7: Building 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
  
• Setting the audio format and voices
  
• Using Speech Synthesis Markup Language
  
• Exercise: Creating a Voice-Enabled Application

Module 8: Speech Translation with Azure AI Speech Service

• Provisioning an Azure resource for speech translation
  
• Voice to text translation
  
• Synthesis of translations
  
• Exercise: Voice Translation

Module 9: Developing 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 should have the following:

• Familiarity with Azure and the Azure Portal
• Experience programming with C# in Python. If you have no previous programming experience, it's 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