Please note: this course is currently under development by Microsoft with an expected release date of 8th June 20. a full course outline will be added as soon as possible. in the meantime please call for further information or to register interest.
Artificial Intelligence (AI) will define the next generation of software solutions and unlocks the potential to create amazing applications that improve life for everyone. This course introduces AI and the Microsoft services students can use to create AI solutions.
The Azure AI Fundamentals course is designed for anyone interested in learning about the types of solutions AI makes possible and the services on Microsoft Azure that are used to create them.
After completing this course, students will be able to:
- Describe Artificial Intelligence workloads and considerations
- Describe fundamental principles of machine learning on Azure
- Describe features of computer vision workloads on Azure
- Describe features of Natural Language Processing (NLP) workloads on Azure
- Describe features of conversational AI workloads on Azure
Module 1: Introduction to AI
In this module, you'll learn about common uses of artificial intelligence (AI), and the different types of workload associated with AI. You'll then explore considerations and principles for responsible AI development.
Lesson 1: Azure AI Fundamentals
- What is Artificial Intelligence?
- Common Artificial Intelligence Workloads
- Artificial Intelligence in Microsoft Azure
- What can you do with Artificial Intelligence?
Lesson 2: Responsible AI
- Challenges and Risks with AI
- Principles of Responsible AI
Module 2: Machine Learning Machine learning is the foundation for modern AI solutions.
In this module, you'll learn about some fundamental machine learning concepts, and how to use the Azure Machine Learning service to create and publish machine learning models.
- Lesson 1: Introduction to Machine Learning
What is machine learning?
Lesson 2: Azure Machine Learning
- What is Azure Machine Learning?
- Automated machine learning
- Azure Machine Learning Designer
Module 3: Computer Vision Computer vision is the area of AI that deals with understanding the world visually, through images, video files, and cameras.
In this module you'll explore multiple computer vision techniques and services
Lesson 1: Computer Vision Concepts
- What is Computer Vision
- Computer Vision Models
- Applications of Computer Vision
Lesson 2: Computer Vision in Azure
- Cognitive Services
- Image Analysis with the Computer Vision Service
- Training Models with the Custom Vision Service
- Analyzing Faces with the Face Service
- Reading Text with the Computer Vision Service
- Analyzing Forms with the Form Recognizer Service
Module 4: Natural Language Processing
This module describes scenarios for AI solutions that can process written and spoken language. You'll learn about Azure services that can be used to build solutions that analyze text, recognize and synthesize speech, translate between languages, and interpret commands
Lesson 1: Introduction to Natural Language Processing
- What is Natural Language Processing?
- Natural Language Processing in Azure
Lesson 2: Using Natural Language Processing Services
- Text Analytics
- Speech Recognition and Synthesis
- Language Understanding
Module 5: Conversational AI Conversational AI enables users to engage in a dialog with an AI agent, or Bot, through communication channels such as email, webchat interfaces, social media, and others.
This module describes some basic principles for working with bots and gives you an opportunity to create a bot that can respond intelligently to user questions.
Lesson 1: Conversational AI Concepts
- What is Conversational AI?
- Responsible AI Guidelines for Bots
Lesson 2: Conversational AI in Azure
- QnA Maker Service
- Azure Bot Service
Students don’t need to have any experience with Microsoft Azure before taking this course; however, a basic level of familiarity with computer technology, cloud computing, and the Internet is assumed. Some of the concepts covered in the course require a basic understanding of mathematics, such as the ability to interpret charts. The course includes hands-on activities that involve working with data and running Python, so knowledge of fundamental programming principles will be helpful.