Microsoft Azure AI Fundamentals (AI-900) Cert Prep by Microsoft Press

Course details

The Microsoft Azure AI Fundamentals (AI-900) certification vets your mastery of core AI concepts related to the development of software and services of Microsoft Azure to create AI solutions. An ideal fit for professionals with both technical and nontechnical backgrounds, this course from Microsoft Press covers basic cloud concepts and client-server applications covered by the exam. The AI-900 certification can also be used to prepare for other Azure role-based certifications such as Azure Data Scientist Associate (DP-100) or Azure AI Engineer Associate (AI-102).

Learning objectives
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 generative AI workloads on Azure.

Concepts

0. Introduction

01 - Introduction

1. Identify Features of Common AI Workloads

02 - Learning objectives
03 - Identify features of content moderation and personalization workloads
04 - Identify computer vision workloads
05 - Identify natural language processing workloads
06 - Identify knowledge mining workloads
07 - Identify document intelligence workloads
08 - Identify features of generative AI workloads

2. Identify Guiding Principles for Responsible AI

09 - Learning objectives
10 - Describe considerations for fairness in an AI solution
11 - Describe considerations for reliability and safety in an AI solution
12 - Describe considerations for privacy and security in an AI solution
13 - Describe considerations for inclusiveness in an AI solution
14 - Describe considerations for transparency in an AI solution
15 - Describe considerations for accountability in an AI solution

3. Identify Common Machine Learning Techniques

16 - Learning objectives
17 - Identify regression machine learning scenarios
18 - Identify classification machine learning scenarios
19 - Identify clustering machine learning scenarios
20 - Identify features of deep learning techniques

4. Describe Azure Machine Learning Capabilities

21 - Learning objectives
22 - Identify features and labels in a dataset for machine learning
23 - Describe how training and validation datasets are used in machine learning
24 - Describe capabilities of automated machine learning
25 - Describe data and compute services for data science and machine learning
26 - Describe model management and deployment capabilities in Azure Machine Learning

5. Identify Common Types of Computer Vision Solutions

27 - Learning objectives
28 - Identify features of image classification solutions
29 - Identify features of object detection solutions
30 - Identify features of optical character recognition solutions
31 - Identify features of facial detection and facial analysis solutions
32 - Describe capabilities of the Azure AI Vision service
33 - Describe capabilities of the Azure AI Face detection service

6. Identify Features of Common NLP Workload Scenarios

34 - Learning objectives
35 - Identify features and uses for key phrase extraction
36 - Identify features and uses for entity recognition
37 - Identify features and uses for sentiment analysis
38 - Identify features and uses for language modeling
39 - Identify features and uses for speech recognition and synthesis
40 - Identify features and uses for translation
41 - Describe capabilities of the Azure AI language service
42 - Describe capabilities of the Azure AI speech service

7. Identify Features of Generative AI Solutions

43 - Learning objectives
44 - Identify features of generative AI models
45 - Identify common scenarios for generative AI
46 - Identify responsible AI considerations for generative AI

8. Identify Capabilities of Azure OpenAI Service

47 - Learning objectives
48 - Describe natural language generation capabilities of Azure OpenAI Service
49 - Describe code generation capabilities of Azure OpenAI Service
50 - Describe image generation capabilities of Azure OpenAI Service

Summary

51 - Next steps