The Complete Guide to Machine Learning with ML.NET

The Complete Guide to Machine Learning with ML.NET

Welcome to Machine Learning with ML.NET! In this introductory video series we will introduce the concept of Machine Learning, what you can do with various tooling, and how to get started with ML.NET!

The Complete Guide to Machine Learning with ML.NET

1. ML.NET - Machine Learning Introduction Welcome to Machine Learning with ML.NET! In this introductory video series we will introduce the concept of Machine Learning, what you can do with various tooling, and how to get started with ML.NET!

2. ML.NET Introduction Learn all about what ML.NET is and what you can do with the Framework. You will also see how customers using it, the open source momentum, and a short demo of the experience.

3.Getting started with ML.NET Learn how you can get started with ML.NET on Windows, Mac, and Linux using tools (ML.NET Model Builder, ML.NET CLI) or code-first using ML.NET API.

4. Build a ML model for Sentiment Analysis In this video, we will cover how to build a ML model for sentiment analysis of customer reviews using a binary classification algorithm.

5. Build a ML model for GitHub Issue classification In the world of ML.NET there is a high-volume of interaction with GitHub. This video will teach you how to classify incoming GitHub issues into one of the many tags (labels) using a multi-class classification algorithm.

6. Build a ML model for predicting taxi fares There are tons of different ways to use the model builder with ML.NET. In this video, we learn how to predict taxi fares based on distance traveled, trip time etc. using a regression algorithm.

7. Build a ML model for Movie Recommendation Here are even more ways to use the model builder with ML.NET. In this video, we learn how to recommend movies for users using colloborative filtering based recommendation approach.

8. Deep learning with ML.NET: Image Classification Deep learning enables many more scenarios using sound, images, text and other data types. Learn how to build an Image Classification model to classify flowers (daisies, roses etc.).

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