Microsoft Azure AI Fundamentals (AI-900) Online Course
Microsoft Azure AI Fundamentals (AI-900) Online Course
Prepare for the AI-900 Certification Exam using Testprep Training course.
We will be covering the following 5 domains in the course:
Domain 1: We will cover the identification of features in common AI workloads and guiding principles for responsible AI.
Domain 2: We will cover the identification of common machine learning variants, description of core machine learning concepts, identification of core risks in the creation of a machine learning solution, and description of capabilities of no-code machine learning with Azure machine learning.
Domain 3: We will cover the identification of common types of computer vision solutions and the identification of Azure tools and services for computer vision tasks.
Domain 4: We will cover the identification of features in common NLP workload scenarios and learn how to identify Azure tools and services for NLP workloads.
Domain 5: We will cover the identification of common use cases for conversational AI and will be identifying Azure services for conversational AI.
By the end of this course, you will be ready to appear for your AI-900 exam.
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
