Natural Language Processing with ML.NET
2h 55mIntermediate2024-05-21
Authors

Microsoft Press
Microsoft

Carlotta Castelluccio
Course details
In today’s world, machine learning is everywhere. We use ML-empowered applications every day—when choosing the next TV series to watch based on Netflix recommendations, for example, or when asking Alexa to play your favorite song. If you think of yourself as a developer, you might see machine learning as a separate art, practiced by an elite group of data scientists and statisticians. You might be uncertain about how it fits into application development. In this course, discover how ML.NET framework is designed to democratize the art of machine learning, making it accessible to all developers and making a smoother experience of integrating a trained model into an existing or a new .NET solution. Learn about ML.NET and how it's different from other ML frameworks, the basics concepts of machine learning and deep learning, and how natural language processing models work.
Skills covered
ML.NETNatural Language Processing (NLP)Software Development ToolsArtificial Intelligence (AI)MicrosoftSoftware DevelopmentOne-Off
Concepts
0. Introduction
- 01 - Natural language processing with ML.NET - Introduction
1. Get started with ML.NET
- 02 - Learning objectives
- 03 - What is machine learning
- 04 - What is the .NET ecosystem
- 05 - What is ML.NET, and how does it differ from other popular machine learning frameworks
- 06 - Exercise - Setting up your local machine to work with ML.NET framework
- 07 - Advanced exercise - Installing and configuring Visual Studio Code and Polyglot notebooks
- 08 - For .NET developers - AI and ML in the .NET ecosystem
2. Classification in ML.NET
- 09 - Learning objectives
- 10 - What is classification
- 11 - Training and evaluating a classification model
- 12 - Exercise - Training a classification model with Model Builder
- 13 - Advanced exercise - Training a classification model with AutoML and Polyglot notebooks
3. Text Classification and Sentence Similarity in ML.NET
- 14 - Learning objectives
- 15 - What is natural language processing (NLP), and how does a NLP model work
- 16 - Text classification task within ML.NET framework
- 17 - Exercise - Fine-tuning a pre-trained NLP model on your data with Model Builder
- 18 - Advanced concepts - Sentence similarity
- 19 - Advanced exercise - Sentence similarity with Model Builder
4. MLOps in ML.NET
- 20 - Learning objectives
- 21 - What is MLOps
- 22 - Deploying and consuming models into the ML.NET framework
- 23 - Exercise - Deploying your .NET application on the cloud
- 24 - Advanced concepts - Azure cloud and GitHub
- 25 - Advanced concepts - Responsible AI
5. Course Wrap-Up and Next Steps
- 26 - Natural language processing with ML.NET - Summary
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