The third lecture from the Machine Learning course series of lectures. It starts with an introduction to idea of unsupervised analysis, and mostly focuses on clustering. Two very popular clustering methods are discussed and compared further: k-means and hierarchical clustering. As both methods need a hyper-parameter k to be chosen beforehand, two very simplistic ways of identifying k are discussed at the end of the lecture. A link to my github (https://github.com/skyfallen/MachineLearningPracticals) with practicals that I have designed for this course in both R and Python. I can share keynote files, contact me via e-mail: email@example.com.