Course Summary

This Microsoft Azure AI Fundamentals (AI-900) course with 180-day access prepares you for the Microsoft Exam AI-900 while helping demonstrate your real-world knowledge of diverse machine learning (ML) and artificial intelligence (AI) workloads, and how they can be implemented with Azure AI. AI-900 focuses on knowledge needed to identify features of common AI workloads and guiding principles for responsible AI; identify common ML types; describe core ML concepts; identify core tasks in creating an ML solution; describe capabilities of no-code ML with Azure Machine Learning Studio; identify common types of computer vision solutions; identify Azure tools and services for computer vision tasks; identify features of common NLP workload scenarios; identify Azure tools and services for NLP workloads; and identify common use cases and Azure services for conversational Al. 

The goal of this course is to provide you with all the tools you need to prepare for the AI-900 Microsoft Azure AI Fundamentals exam — including text explanations, video demos, lab activities, self-assessment questions, and a practice exam— to increase your chances of passing the exam on your first try.

Methodology:

180-day access to:

  • Lessons
  • Video learning
  • MeasureUp Practice Test for Microsoft AI-900. Practice Mode with remediation and Certification mode to simulate the test day experience.
Duration: 

Approximately 20 hours of primary content. Each learner will progress at their own rate.

Audience: 

Designed for business stakeholders, new and existing IT professionals, consultants, and students, this course focuses on the critical thinking and decision-making acumen needed for success at the Microsoft Certified: Azure AI Fundamentals level.

Prerequisites:
  • The students should have some basic knowledge of operating systems and computer networks.
  • The students should be familiar with the usage of the Internet and web browsers.
Course Outline:

Upon successful completion of this course, students should be able to:   

  • Describe AI 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.

Lesson 1: Describe Artificial Intelligence workloads and considerations (3 hours)

  • Skill 1.1: Identify features of common AI workloads (1 hour and 12 minutes).
    • Identify features of common AI workloads.
    • Describe Azure services for AI and ML.
    • Understand Azure Machine Learning.
    • Understand Azure Cognitive Services.
    • Describe the Azure Bot Service.
    • Identify common AI workloads.
  • Skill 1.2: Identify guiding principles for Responsible AI (1 hour and 24 minutes).
    • Identify guiding principles for Responsible AI.
    • Describe the Fairness principle.
    • Describe the Reliability & Safety principle.
    • Describe the Privacy & Security principle.
    • Describe the Inclusiveness principle.
    • Describe the Transparency principle.
    • Describe the Accountability principle.
    • Understand Responsible AI for Bots.
    • Understand Microsoft’s AI for Good program.
  • Summary
  • Case Study (12 minutes)
  • Quiz (12 minutes)

 

Lesson 2: Describe fundamental principles of machine learning on Azure (4 hours and 36 minutes).

  • Skill 2.1: Identify common machine learning types (1 hour).
    • Identify common machine learning types.
    • Understand machine learning model types.
    • Describe regression models.
    • Describe classification models.
    • Describe clustering models.
  • Skill 2.2: Describe core machine learning (1 hour and 12 minutes).
    • Describe core machine learning.
    • Understand the machine learning workflow.
    • Identify the features and labels in a dataset for machine learning.
    • Describe how training and validation datasets are used in machine learning.
    • Describe how machine learning algorithms are used for model training.
    • Select and interpret model evaluation metrics (1 hour and 24 minutes).
  • Skill 2.3: Identify core tasks in creating a machine learning solution.
    • Identify core tasks in creating a machine learning solution.
    • Understand machine learning on Azure.
    • Understand Azure Machine Learning studio.
    • Describe data ingestion and preparation.
    • Describe feature selection and engineering.
    • Describe model training and evaluation.
    • Describe model deployment and management.
  • Skill 2.4: Describe capabilities of no-code machine learning with Azure Machine Learning (36 minutes).
    • Describe capabilities of no-code machine learning with Azure Machine Learning.
    • Describe Azure Automated Machine Learning.
    • Describe Azure Machine Learning designer.
  • Summary
  • Case Study (12 minutes)
  • Quiz (12 minutes)

 

Lesson 3: Describe features of computer vision workloads on Azure (2 hours and 48 minutes).

  • Skill 3.1: Identify common types of computer vision solution (1 hour and 24 minutes).
    • Identify common types of computer vision solution.
    • Introduce Cognitive Services.
    • Understand computer vision.
    • Describe image classification.
    • Describe object detection.
    • Describe optical character recognition.
    • Describe facial detection, recognition, and analysis.
  • Skill 3.2: Identify Azure tools and services for computer vision tasks (1 hour).
    • Identify Azure tools and services for computer vision tasks.
    • Understand the capabilities of the Computer Vision service.
    • Understand the Custom Vision service.
    • Understand the Face service.
    • Understand the Form Recognizer service.
  • Summary
  • Case Study (12 minutes)
  • Quiz (12 minutes)

Lesson 4: Describe features of Natural Language Processing (NLP) workloads on Azure (2 hours and 48 minutes).

  • Skill 4.1: Identify features of common NLP workload scenarios (1 hour and 24 minutes).
    • Identify features of common NLP workload scenarios.
    • Describe Natural Language Processing.
    • Describe language modeling.
    • Describe key phrase extraction.
    • Describe named entity recognition.
    • Describe sentiment analysis.
    • Describe speech recognition and synthesis.
  • Skill 4.2: Identify Azure tools and services for NLP workloads (1 hour).
    • Identify Azure tools and services for NLP workloads.
    • Identify the capabilities of the Text Analytics service.
    • Identify the capabilities of the Language Understanding service (LUIS).
    • Identify the capabilities of the Speech service.
    • Identify the capabilities of the Translator service.
  • Summary
  • Case Study (12 minutes)
  • Quiz (12 minutes)

Lesson 5: Describe features of conversational workloads on Azure (1 hour and 36 minutes).

  • Skill 5.1: Identify common use cases for conversational AI (36 minutes).
    • Identify common use cases for conversational AI.
    • Identify features and uses for webchat bots.
    • Identify common characteristics of conversational AI solutions.
  • Skill 5.2: Identify Azure services for conversational AI (36 minutes).
    • Identify Azure services for conversational AI.
    • Identify capabilities of the QnA Maker service.
    • Identify capabilities of the Azure Bot Service.
  • Summary
  • Case Study (12 minutes)
  • Quiz (12 minutes)