Microsoft Azure AI Fundamentals (AI-900) Online Course

Microsoft Azure AI Fundamentals (AI-900) Online Course

Get fully prepared for the AI-900 certification exam with this comprehensive course from Testprep Training. This course covers all five key domains of the Microsoft Azure AI Fundamentals exam, equipping you with the foundational knowledge needed to succeed.

Course Coverage Includes:

Domain 1: Understand common AI workloads and the principles of responsible AI.

Domain 2: Learn about machine learning types, core concepts, risks in ML development, and no-code ML capabilities using Azure Machine Learning.

Domain 3: Explore computer vision solutions and Azure tools for computer vision tasks.

Domain 4: Identify features of natural language processing (NLP) workloads and the Azure services that support them.

Domain 5: Understand conversational AI use cases and the corresponding Azure services.

By the end of this course, you'll be fully prepared to take and pass the AI-900 exam with confidence.

Who should take this Course?

The Microsoft Azure AI Fundamentals (AI-900) Online Course is ideal for beginners, students, business professionals, and IT personnel who want to gain foundational knowledge of artificial intelligence concepts and Microsoft Azure AI services. It’s also suitable for individuals preparing for the AI-900 certification exam and those looking to explore AI applications without requiring prior programming or data science experience.

Course Table of Contents

Introduction

• Course Introduction

Azure Portal Introduction: For Beginners

• Create Azure Free Subscription
• Azure Portal Overview
• Azure Sandbox - How to Use Azure Portal Absolutely Free

AI Workloads and Considerations (15-20%)

• Learning Objectives
• What is Artificial Intelligence
• Prediction and Forecasting
• Anomaly Detection Workloads
• Computer Vision Workloads
• Natural Language Processing
• Knowledge Mining Workloads
• Conversational AI Workloads
• Introduction to Guiding Principles of Responsible AI
• Guiding Principle - Fairness
• Guiding Principle - Reliability and Safety
• Guiding Principle - Privacy and Security
• Guiding Principle - Inclusiveness
• Guiding Principle - Transparency
• Guiding Principle - Accountability

Fundamental Principles of Machine Learning on Azure (30- 35%)

• Learning Objectives
• Introduction to Machine Learning
• Rule-Based Versus Machine Learning Based Learning
• Classification Versus Regression Versus Clustering Machine Learning Types
• Feature Selection and Feature Engineering
• Training Versus Validating Dataset
• Machine Learning Algorithms
• Demo Part 1.1 ML Workspace
• Demo Part 1.2 Regression Model
• Demo Part 1.3 Delete Resources
• Demo 2.1 Classification Model
• Demo 3.1 Automated Machine Learning
• Demo: Delete Compute

Describe Features of Computer Vision Workloads on Azure (15-20%)

• Learning Objectives
• Image Classification Versus Object Detection Versus Semantic Segmentation
• Optical Character Recognition (OCR)
• Face Detection Recognition and Analysis
• What are Cognitive Services
• What are Computer Vision Services
• Demo: Computer Vision
• Custom Vision Service
• Demo: Custom Vision Service
• Face Service
• Form Recognizer Service

Natural Language Processing (NLP) Workloads on Azure (15-20%)

• Learning Objectives
• What is Natural Language Processing
• Key Phrase Extraction Versus Entity Recognition Versus Sentiment Analysis
• Language Modelling
• Speech Recognition and Speech Synthesis
• Translation
• Introduction to Azure Tools and Services for NLP
• Text Analytics Service
• Speech Service
• Translator Service
• Language Understanding Service (LUIS)

Conversational AI Workloads on Azure (15-20%)

• Learning Objectives
• Conversational AI Use Cases
• QnA Maker and Bot Framework
• Demo QnA Maker and Bot Framework