Gaussian Mixture Model

# Introduction

This lab will guide you through the implementation of Gaussian Mixture Models (GMMs) using the scikit-learn library in Python. GMMs are probabilistic models that assume that the data is generated from a mixture of several Gaussian distributions. They are widely used in various fields such as computer vision, finance, and bioinformatics for clustering and density estimation tasks.

## VM Tips

After the VM startup is done, click the top left corner to switch to the **Notebook** tab to access Jupyter Notebook for practice.

Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook.

If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.