In previous chapters, our work centered on supervised learning, where algorithms learn from data that includes known outcomes or labels. This chapter shifts focus to unsupervised learning. This area of machine learning deals with datasets that lack predefined labels. Here, the objective is for algorithms to independently identify patterns, structures, or relationships within the data.

We will also address dimensionality reduction. Datasets with many features can present challenges for modeling and computation. Dimensionality reduction techniques aim to reduce the number of input variables while retaining meaningful properties of the data. This can lead to simpler models, faster computations, and can assist in visualizing data by projecting it into a lower-dimensional space, for instance, from $R^n$ to $R^2$ or $R^3$.

Throughout this chapter, you will learn to implement several widely used unsupervised learning algorithms in Julia. We will cover clustering methods such as K-Means, which segments data into a specified number $k$ of clusters, and DBSCAN, a density-based approach. Additionally, you will work with Principal Component Analysis (PCA), a common technique for reducing data dimensions. Finally, we'll examine approaches to evaluate the performance of your clustering results.