Go beyond a surface-level understanding of KMeans. This guide provides a complete breakdown of the algorithm, starting with a practical look at tuning key Scikit-learn hyperparameters like n_clusters and init. We then dive into the crucial concepts of time and space complexity to understand how KMeans performs on large datasets. Finally, we explore the core mathematical objective, the challenges of finding an optimal solution, and how Lloyd's Algorithm works in practice.

Unsupervised learning is a type of machine learning that uncovers hidden patterns in data without labels. Discover its key types, from clustering and dimensionality reduction to anomaly detection, and see how these techniques are applied in real-world scenarios like customer segmentation and image processing.