Using Built-In Supervised Learning Tasks

Explore how to apply various supervised learning tasks in ML.NET, including classification, regression, ranking, recommendations, and forecasting. Understand how to select the appropriate task for your problem and prepare data effectively for building accurate machine learning models.

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Performing supervised learning in ML.NET

ML.NET has a wide range of supervised ML tasks available in its CLI and the NuGet libraries. These include the following:

Classification: This is where the model is built to determine whether an entity belongs to a specific category based on its parameters. For example, we might want to build a model that identifies a type of car based on its dimensions, the number of seats, engine power, etc. ML.NET supports binary classification, multi-class classification, and image classification.
Regression: Unlike classification, which deals with categorical labels, regression is used to predict a calculated value. For example, we can train the model by using item descriptions and prices in a shop and then use it to predict a price for an item it has never seen before.
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1.Introduction to ML.NET

2.Machine Learning Fundamentals

3.Selecting a Problem for Machine Learning

4.Built-In Supervised Learning Tasks in ML.NET

5.Built-In Unsupervised Learning Tasks in ML.NET

6.Deep Learning and Neural Networks

7.Automating Machine Learning Tasks with AutoML

8.Saving and Consuming Machine Learning Models

Mini Project

9.Wrapping Up

Project

10.Appendix

Using Built-In Supervised Learning Tasks