AI Development with .NET

Build Intelligent Applications with ML.NET and Azure AI

Artificial intelligence is changing how software is built, and now you can be part of this transformation. In this course, you will learn how to bring machine learning into your .NET applications using ML.NET in a simple and practical way. You don’t need prior experience in AI or data science, this course guides you step by step from the basics to building real AI-powered features.

You will gain hands-on experience training machine learning models, working with real datasets, and integrating predictions into your own .NET applications. You will also explore modern Azure AI Services such as Azure OpenAI, Vision, and Document Intelligence. By the end of the course, you will have the skills and confidence to start building intelligent applications and take your development career to the next level.

What You will Learn:

• AI and Machine Learning Fundamentals: Understand core AI concepts and how machine learning differs from traditional programming approaches.
• Model Training with ML.NET: Build and train machine learning models using ML.NET APIs, Model Builder, and AutoML tools.
• Data Preparation and Structuring: Learn how to prepare datasets with features and labels for classification and regression tasks.
• Model Integration in .NET Applications: Integrate trained machine learning models into .NET applications to generate real-time predictions.
• Practical AI Scenarios: Develop real-world use cases such as sentiment analysis and numeric prediction.
• Local vs Cloud AI Solutions: Identify when to use ML.NET models versus cloud-based AI services based on project requirements.
• Azure AI Services Overview: Understand how Azure OpenAI, Vision, and Document Intelligence support enterprise AI workflows.

Topics Covered

• Introduction to AI and Machine Learning: Core concepts, terminology, and how AI is applied in modern applications.
• Types of Machine Learning: Classification, regression, and clustering techniques and their use cases.
• Machine Learning Workflows: Model lifecycles including data collection, training, evaluation, and deployment.
• ML.NET Ecosystem: Overview of ML.NET tools and integration within the .NET platform.
• Code-First Model Training: Training models using ML.NET APIs and building custom pipelines.
• No-Code Model Builder: Using ML.NET Model Builder for visual, no-code model training.
• AutoML Techniques: Automated model selection and optimization for better performance.
• Working with Datasets: Features, labels, and data preparation techniques.
• Building Classification Models: Creating and evaluating classification-based machine learning solutions.
• Model Integration: Embedding trained ML models into .NET applications for predictions.
• Azure AI Services: Overview of Azure OpenAI, Vision, and Document Intelligence services.
• Azure AI Demonstrations: Practical demonstrations of OpenAI, Vision, and Document Intelligence in real scenarios.

Who Should Take This Course

• .NET developers who want to add AI and machine learning capabilities to their applications
• Software engineers interested in practical, real-world AI development
• Students and recent graduates seeking in-demand AI and .NET skills
• Technical professionals with no prior machine learning experience

Course Details and Next Steps

• Prerequisite: C# Programming II: Object Oriented Programming or equivalent knowledge and experience.
• Next Step: Upon completion of this course, consider taking courses in our .NET Development with C# certificate program.
• Contact: For more information about this course, please email infotech@ucsd.edu.