Summary  
This chapter explains Gaussian mixture models as an iterative probabilistic clustering method that estimates multiple Gaussian component parameters via ELBO optimization and contrasts it with K-means’ hard assignments and spherical clusters.

General domain of usage  
Unsupervised clustering of overlapping or non-spherical data distributions.

The **Gaussian mixture model** is a versatile clustering algorithm that addresses the limitations of methods like **K-means** by handling overlapping clusters and complex data distributions. Throughout this section, you saw its effectiveness on both synthetic and real-world datasets. 

In summary, GMM provides a more robust solution for clustering tasks involving **overlapping** and **non-spherical** clusters, making it ideal for more complex datasets. 

What is the main advantage of GMM over K-means?

Explore the power of hidden patterns with unsupervised learning. Master the most influential clustering algorithms, including K-Means, Hierarchical Clustering, DBSCAN, and Gaussian Mixture Models. Learn to evaluate cluster quality using WSS and Silhouette scores, handle diverse distance measures, and implement robust solutions on real-world datasets. Build the skills to segment customers and discover structures in unlabeled data using Scikit-learn.

Conclusion