AI-ML

This one-day course is composed of two parts – Getting started with Microsoft 365 Copilot and Empower your workforce with Microsoft 365 Copilot Use Cases. The first part of the course introduces you to Copilot for Microsoft 365, examines how you can use Copilot throughout the various Microsoft 365 apps, explores best practices for using Copilot and building effective prompts, and examines how you can extend Copilot.
The second part of this training content is really the heart of this course. Students perform a series of hands-on exercises involving seven Use Cases – Executives, Sales, Marketing, Finance, IT, HR, and Operations. These exercises focus on using Copilot in various Microsoft 365 apps (such as Word, PowerPoint, Outlook, and so on) to complete a series of common business-related tasks pertaining to each Use Case. To complete the Use Case exercises in this course, each student must have access to a Microsoft 365 subscription (BYOS) in which they’re licensed to use Microsoft 365 Copilot. Each student must also have a Microsoft OneDrive account, since Copilot requires OneDrive to complete the file sharing tasks used throughout the Use Case exercises.

Learn the knowledge and skills to drive enablement of Microsoft 365 Copilot across your organization. Explore effective strategies, including the Microsoft 365 Copilot user enablement framework to help you create and implement a robust adoption plan.

This course begins by examining the Microsoft Copilot for Microsoft 365 design. Its main focus, however, is on the security and compliance features that administrators must configure in their Microsoft 365 tenant to protect their company’s organizational data before they implement Copilot for Microsoft 365.

Discover ways to craft effective and contextual prompts for Microsoft 365 Copilot that create, simplify, transform, and compile content across Microsoft 365 applications. Learn the importance of providing a clear goal, context, source, and expectation in your prompt for the best results. This course covers real world scenarios and examples using Copilot in Microsoft 365 apps like Word, Excel, PowerPoint, Teams, Outlook, OneNote, and Chat.

To train a machine learning model with Azure Machine Learning, you need to make data available and configure the necessary compute. After training your model and tracking model metrics with MLflow, you can decide to deploy your model to an online endpoint for real-time predictions. Throughout this learning path, you explore how to set up your Azure Machine Learning workspace, after which you train and deploy a machine learning model.

Computer vision is an area of artificial intelligence that deals with visual perception. Azure AI Vision includes multiple services that support common computer vision scenarios.

Natural language processing (NLP) solutions use language models to interpret the semantic meaning of written or spoken language. You can use the Language Understanding service to build language models for your applications.

In this learning path, you’ll see how Azure AI Document Intelligence solutions can enable you to capture data from typed or hand-written forms. You’ll also learn how to build a solution for your custom form types and integrate that solution into an Azure Cognitive Search pipeline.

This course introduces fundamentals concepts related to artificial intelligence (AI), and the services in Microsoft Azure that can be used to create AI solutions. 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.

Artificial intelligence (AI) and machine learning (ML) have become an essential part of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users. This course includes hands on activities for each topic area.