The training algorithms can be categorised into supervised, unsupervised, or reinforcement learning. Semi-supervised learning, a related variant, makes use of supervised and unsupervised techniques. Recent research has increasingly focused on unsupervised and semi-supervised learning algorithms. These methods can roughly be divided into two categories, unsupervised and supervised models. Deep learning algorithms can be applied to unsupervised learning tasks. Conceptual clustering developed mainly during the 1980s, as a machine paradigm for unsupervised learning. Therefore, autoencoders are unsupervised learning models. While the data processing can be done in many ways, learning is not completely unsupervised. Complicated regulations and arbitrary, unsupervised official conduct exacerbate the problem. Waterfall is entirely unsupervised, requiring no prior information, and produces linear trajectories. Turner died prematurely due to drowning during presumed unsupervised ketamine use. Therefore, autoencoders are unsupervised learning models (do not require labeled inputs to enable learning). One of the popular methods in unsupervised classification is k-means clustering. In unsupervised feature learning, features are learned with unlabeled input data. One of the statistical approaches for unsupervised learning is the method of moments.