Microsoft Azure AI Fundamentals (AI-900)

Category ID Duration Level Price
Azure 13777 1 Day(s) Foundation $795.00

Microsoft Azure AI Fundamentals (AI-900)

Live, Online Class Schedule

Delivery Format Date Time Register
Live Online 12/20/2024 - 12/20/2024 10:00 AM - 4:45 AM EST Register
Live Online 01/31/2025 - 01/31/2025 9:00 AM - 5:00 PM EST Register
Live Online 03/24/2025 - 03/24/2025 9:00 AM - 5:00 PM EDT Register
Live Online 05/02/2025 - 05/02/2025 9:00 AM - 5:00 PM EDT Register

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Microsoft Azure AI Fundamentals (AI-900)

Course Overview

The course is not designed to teach students to become professional data scientists or software developers, but rather to build awareness of common AI workloads and the ability to identify Azure services to support them. The course is designed as a blended learning experience that combines instructor-led training with online materials on the Microsoft Learn platform (https://azure.com/learn). The hands-on exercises in the course are based on Learn modules, and students are encouraged to use the content on Learn as reference materials to reinforce what they learn in the class and to explore topics in more depth.

One Microsoft exam voucher is included with the Microsoft Azure AI Fundamentals (AI-900) Training Course.

Microsoft Azure AI Fundamentals (AI-900)

Course Objectives

After successfully completing this course, you will be able to:
• 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

Exam AI-900: Microsoft Azure AI Fundamentals

Course Prerequisites

Prerequisite certification is not required before taking this course. Successful Azure AI Fundamental students start with some basic awareness of computing and internet concepts, and an interest in using Azure AI services. Specifically:
• Experience using computers and the internet.
• Interest in use cases for AI applications and machine learning models.
• A willingness to learn through hands-on exploration.

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