Clustering

Explore how to implement clustering using ML.NET's KMeansTrainer to group similar data points and uncover natural patterns in unlabeled datasets. Understand the process from building a training pipeline to using a trained model for predictions, and learn the fundamentals of K-means clustering in real-world applications.

We'll cover the following...

Clustering application structure
- Building the model
- Consuming the model
Clustering trainers
- KMeansTrainer

Clustering is an ML task that involves grouping similar data points together based on their inherent patterns or similarities. The goal of clustering is to discover the underlying structure in unlabeled data and identify natural groupings or clusters.

Clustering has a wide range of applications, including customer segmentation, image segmentation, document clustering, and market research. It helps in identifying meaningful groups within data, uncovering hidden patterns, and providing insights for decision-making and further analysis.

Clustering application structure

ML.NET ...

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

Clustering

Clustering application structure