Lessons

• Introduction to Artificial Intelligence
• Artificial Intelligence in Azure

Cognitive Services are the core building blocks for integrating AI capabilities into your apps. In this module, you’ll learn how to provision, secure, monitor, and deploy cognitive services.

Lessons

• Getting Started with Cognitive Services
• Using Cognitive Services for Enterprise Applications

Lab:

• Get Started with Cognitive Services Lab
• Manage Cognitive Services Security Lab
• Monitor Cognitive Services Lab
• Use a Cognitive Services Container

After completing This module, students will be able to:

• Provision and consume cognitive services in Azure.
• Manage cognitive services security.
• Monitor cognitive services.
• Use a cognitive service container.

Natural Language Processing (NLP) is a branch of artificial intelligence that deals with extracting insights from written or spoken language. In this module, you’ll learn how to use cognitive services to analyze and translate text.

Lessons

• Analyzing Text
• Translating Text

Lab:

• Analyze Text Lab
• Translating Text

After completing This module, students will be able to:

• Use the Text Analytics cognitive service to analyze text.
• Use the Translator cognitive service to translate text.

Many modern apps and services accept spoken input and can respond by synthesizing text. In this module, you’ll continue your exploration of natural language processing capabilities by learning how to build speech-enabled applications.

Lessons

• Speech Recognition and Synthesis
• Speech Translation

Lab:

• Recognize and Synthesize Speech Lab
• Translate Speech

After completing This module, students will be able to:

• Use the Speech cognitive service to recognize and synthesize speech.
• Use the Speech cognitive service to translate speech.

To build an application that can intelligently understand and respond to natural language input, you must define and train a model for language understanding. In this module, you’ll learn how to use the Language Understanding service to create an app that can identify user intent from natural language input.

Lessons

• Creating a Language Understanding App
• Publishing and Using a Language Understanding App
• Using Language Understanding with Speech

Lab:

• Create a Language Understanding App Lab
• Create a Language Understanding Client Application Lab
• Use the Speech and Language Understanding Services

After completing This module, students will be able to:

• Create a Language Understanding app.
• Create a client application for Language Understanding
• Integrate Language Understanding and Speech

One of the most common kinds of interaction between users and AI software agents is for users to submit questions in natural language and for the AI agent to respond intelligently with an appropriate answer. In this module, you’ll explore how the QnA Maker service enables the development of this kind of solution.

Lessons

• Creating a QnA Knowledge Base
• Publishing and Using a QnA Knowledge Base

Lab:

• Create a QnA Solution

After completing This module, students will be able to:

• Use QnA Maker to create a knowledge base
• Use a QnA knowledge base in an app or bot

Bots are the basis for an increasingly common kind of AI application in which users engage in conversations with AI agents, often as they would with a human agent. In this module, you’ll explore the Microsoft Bot Framework and the Azure Bot Service, which together provide a platform for creating and delivering conversational experiences.

Lessons

• Bot Basics
• Implementing a Conversational Bot

Lab:

• Create a Bot with the Bot Framework SDK Lab
• Create a Bot with Bot Framework Composer

After completing This module, students will be able to:

• Use the Bot Framework SDK to create a bot
• Use the Bot Framework Composer to create a bot

Computer vision is an area of artificial intelligence in which software applications interpret visual input from images or video. In this module, you’ll start your exploration of computer vision by learning how to use cognitive services to analyze images and video.

Lessons

• Analyzing Images
• Analyzing Videos

Lab:

• Analyze Images with Computer Vision Lab
• Analyze Video with Video Indexer

After completing This module, students will be able to:

• Use the Computer Vision service to analyze images
• Use Video Indexer to analyze videos

While there are many scenarios where pre-defined general computer vision capabilities can be useful, sometimes you need to train a custom model with your own visual data. In this module, you’ll explore the Custom Vision service, and how to use it to create custom image classification and object detection models.

Lessons

• Image Classification
• Object Detection

Lab:

• Classify Images with Custom Vision Lab
• Detect Objects in Images with Custom Vision

After completing This module, students will be able to:

• Use the Custom Vision service to implement image classification
• Use the Custom Vision service to implement object detection

Facial detection, analysis, and recognition are common computer vision scenarios. In this module, you’ll explore the user of cognitive services to identify human faces.

Lessons

• Detecting Faces with the Computer Vision Service
• Using the Face Service

Lab:

• Detect, Analyze, and Recognize Faces

After completing This module, students will be able to:

• Detect faces with the Computer Vision service
• Detect, analyze, and recognize faces with the Face service

Optical character recognition (OCR) is another common computer vision scenario, in which software extracts text from images or documents. In this module, you’ll explore cognitive services that can be used to detect and read text in images, documents, and forms.

Lessons

• Reading text with the Computer Vision Service
• Extracting Information from Forms with the Form Recognizer service

Lab:

• Read Text in ImagesLab
• Extract Data from Forms

After completing This module, students will be able to:

• Use the Computer Vision service to read text in images and documents
• Use the Form Recognizer service to extract data from digital forms

Ultimately, many AI scenarios involve intelligently searching for information based on user queries. AI- powered knowledge mining is an increasingly important way to build intelligent search solutions that use AI to extract insights from large repositories of digital data and enable users to find and analyze those insights.

Lessons

• Implementing an Intelligent Search Solution
• Developing Custom Skills for an Enrichment Pipeline
• Creating a Knowledge Store

Lab:

• Create an Azure Cognitive Search solution Lab
• Create a Custom Skill for Azure Cognitive Search Lab
• Create a Knowledge Store with Azure Cognitive Search

After completing This module, students will be able to:

• Create an intelligent search solution with Azure Cognitive Search
• Implement a custom skill in an Azure Cognitive Search enrichment pipeline
• Use Azure Cognitive Search to create a knowledge store

Lessons

• What is Generative AI
• Getting Started with Azure OpenAI Service
• Azure OpenAI Studio
• Types of Generative AI models
• Deploying generative AI models

Lab:

• Deploying Azure OpenAI service
• Using Azure OpenAI in your App
• Utilize prompt engineering in your app
• Generate and improve code with Azure OpenAI Service
• Generate images with a DALL-E model
• Implement Retrieval Augmented Generation (RAG) with Azure OpenAI Service

After completing This module, students will be able to:

• Create and deploy Azure OpenAI resources
• Integrated Azure OpenAI into your application through REST APIs and SDKs
• Explored prompt engineering techniques to improve model responses
• Connected own data for grounding an Azure OpenAI model