Starting with a review of the principal component analysis (PCA), the chapter explores canonical algorithms of unsupervised learning. It presents cluster approaches like K-Means, Mini-Batch-K-Means and the t-Student distributed neighbour embedding (t-SNE). For all techniques, Python examples are described that help to understand the application of the respective method in a simplified, yet technically motivated use case. The Autoencoder is presented based as an advanced form of PCA and discussed in one of its most commonly used applications: the anomaly detection.