LinkedIn – Leveraging GenAI in .NET MAUI Development 2024-7

LinkedIn – Leveraging GenAI in .NET MAUI Development 2024-7 Downloadly IRSpace

LinkedIn – Leveraging GenAI in .NET MAUI Development 2024-7
LinkedIn – Leveraging GenAI in .NET MAUI Development 2024-7

Leveraging GenAI in .NET MAUI Development course. In this course, you will learn how to use the power of generative artificial intelligence to increase your productivity in developing .NET MAUI applications. Generative AI can dramatically speed up your app development process. In this course, we’ll show you what tools are available and how you can make the most of them. From a comprehensive introduction to the benefits of generative AI for MAUI developers, to working with GitHub Copilot and UI design, this course covers it all. Also, you will learn how to generate custom content for your MAUI applications. Each main part of this course is accompanied by practical exercises so that you can learn the material well.

What you will learn:

  • Comprehensive introduction: Deep understanding of the benefits of generative artificial intelligence in MAUI development
  • GitHub Copilot: Use this powerful tool to speed up coding
  • User interface design: generating creative design ideas with the help of artificial intelligence
  • Content creation: Create custom content for MAUI applications

This course is suitable for people who:

  • .NET MAUI developers looking to increase their productivity.
  • They are interested in using new technologies in application development.
  • They want to learn about productive artificial intelligence tools.

Specifications of the Leveraging GenAI in .NET MAUI Development course

  • Publisher:  LinkedIn
  • Instructor: Kevin Ford
  • Training level: beginner to advanced
  • Training duration: 2 hours and 39 minutes

Course headings

 Leveraging GenAI in .NET MAUI Development

Images of Leveraging GenAI in .NET MAUI Development course

Leveraging GenAI in .NET MAUI Development

Sample video of the course

Installation guide

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Download file – 353 MB

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353 MB