The fourth lecture from the Machine Learning course series of lectures. This lecture first introduces a problem of visualising multi-dimensional data on fewer dimensions and later discusses one of the most popular methods for reducing dimensionality - principal component analysis (PCA). Later, also t-SNE is mentioned briefly as a non-linear alternative to PCA. 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.