Microsoft Azure AI Fundamentals (AI-900) Online Course
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