Introduction to ML.NET

Explore the world of ML.NET in this comprehensive conference talk. Dive into the vision and architecture of Microsoft's machine learning framework designed specifically for .NET developers. Learn how to infuse custom AI into existing .NET applications using both code-driven and UI-driven approaches. Discover various scenarios enabled by ML.NET, including face detection, GitHub issue classification, and content-based filtering. Gain insights into supervised learning, multiclass classification, and multiple labels. Follow along with practical demonstrations and examples, including building pipelines and console applications. Understand key concepts such as numeric vectors, factorization, and the essence of machine learning. By the end of this talk, grasp how ML.NET has been utilized within Microsoft by Windows, Azure, SQL, and Bing, and how it can empower .NET developers to integrate powerful machine learning capabilities into their projects.

Introduction
Machine Learning
Is this a face
How do we build functions
Data
Classification
Cognitive Services
Building Models
Agenda
MLNET
GitHub Issue Classification
MultiClass Classification
Multiple Labels
Console Application
Pipeline
GitHub Issue
Pipeline Example
Supervised Learning
Label
Title
Numeric Vector
Glossary
Review
Favorite movies
Netflix
Population averages
Contentbased filtering
Data set
Factorization
Essence of Machine Learning
Demo