Unsupervised Learning: Clustering using Gaussian Mixture Model (GMM)

Clustering is a fundamental task in unsupervised machine learning that involves grouping data points based on their similarity. Gaussian mixture model (GMM) is a popular clustering method that models the data as a mixture of Gaussian distributions. GMM is a probabilistic clustering method that assigns a probability distribution to each cluster, allowing for more flexible and accurate clustering than other methods. GMM can model complex cluster shapes and can handle overlapping clusters. GMM is also useful for density estimation, which involves estimating the probability distribution of a set of data points.

In this tutorial, we will cover the theoretical background of GMM and its implementation in Python using the scikit-learn library. We will also discuss examples and applications of GMM in image segmentation and customer segmentation. Finally, we will examine the limitations and extensions of GMM, including its sensitivity to noise and outliers, and explore alternative clustering methods.

By the end of this tutorial, you should have a good understanding of how GMM works, how to implement it in Python, and how to use it for clustering and density estimation. You should also be familiar with the advantages and limitations of GMM and how to choose the appropriate clustering method for your data and goals. This tutorial is suitable for anyone with a basic understanding of Python and machine learning.