AI Development
Learn to build real-world AI applications with .NET, covering fundamentals, core techniques, and practical samples.
Learn from: the original creator. Original content published on YouTube. DigitalSkillX organizes these public resources into a structured learning path. DigitalSkillX does not claim ownership or partnership.
Now playing: .NET GenAI Fundamentals Review
This learning path is based on the official .NET Generative AI course. It starts with the basics of AI development in .NET, then covers core techniques like completions, function calling, RAG, vision, audio, and agents. Finally, you'll explore practical sample applications that show how to put these concepts into action. Whether you're new to AI or an experienced .NET developer, this path gives you a structured way to learn and apply generative AI with .NET.
Beginners who want a structured free introduction before deeper practice.
Lesson 1: .NET GenAI Fundamentals Review
This lesson introduces the Generative AI for .NET course. It covers the fundamentals of AI development, including tools like Semantic Kernel, MEAI, and GitHub Models, and sets the stage for building real-world applications.
Open on YouTubeLesson 2: Setting Up Your .NET GenAI Environment
Learn how to set up your .NET development environment for AI projects. The lesson covers using Codespaces, MEAI, and other tools to create a robust setup for building AI-powered applications.
Open on YouTubeLesson 3: Core GenAI Techniques - Completions and Chat
This video covers core GenAI techniques for completions and chat scenarios. You'll learn how to generate text and build chat applications using .NET.
Open on YouTubeLesson 4: Core GenAI Techniques - Function Calling
Explore function calling in AI applications. This lesson explains how to integrate LLM functions into .NET workflows to enhance interaction and automate tasks.
Open on YouTubeLesson 5: Core GenAI Techniques - Retrieval Augmented Generation
Learn about Retrieval-Augmented Generation (RAG) in .NET. This technique allows AI to generate responses based on large datasets or specific contexts, making it ideal for enterprise applications.
Open on YouTubeLesson 6: Core GenAI Techniques - Vision
This lesson dives into vision-based AI with .NET. You'll discover how to generate, interpret, and manipulate images using multimodal models, focusing on image recognition and generation.
Open on YouTubeLesson 7: Core GenAI Techniques - Real-time Audio
Learn about real-time audio techniques in .NET. This video covers generating, transcribing, and manipulating audio, useful for speech synthesis and advanced audio applications.
Open on YouTubeLesson 8: Core GenAI Techniques - Agents
Understand AI agents and how they work in .NET. This lesson explores autonomous AI applications that perform tasks using workflows, plugins, and LLM architectures.
Open on YouTubeLesson 9: Practical Samples - Overview
This lesson introduces practical samples for .NET + AI. It covers real-world scenarios like chat and vision, including setups for local and Azure AI models.
Open on YouTubeLesson 10: Practical Samples - eShopLite Semantic Search
Learn how eShopLite implements semantic search. This reference app demonstrates using in-memory vector databases and Azure AI Search to create intelligent search features in eCommerce sites.
Open on YouTubeLesson 11: Practical Samples - Chat with Your Data (RAG)
Build a 'Chat with your Data' application. This lesson focuses on using semantic search, vector databases, and Azure AI Search to create a chat app powered by document content.
Open on YouTubeLesson 12: Practical Samples - Creative Writing Agents
Discover creative writing with agents. This lesson walks through building a writing assistant using Semantic Kernel, .NET Aspire, and modern frontend tools like React and Vite.
Open on YouTubeLesson 13: Practical Samples - eShopLite with Real-time Audio
See real-time AI in eShopLite. This lesson shows how the app integrates GPT-4 for semantic search and real-time audio capabilities in an eCommerce environment.
Open on YouTube0 of 13 lessons completed (0%).
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