unsupervised learning and preprocessing the second family of machine learning algorithms that we will discuss is unsupervised learning algorithms unsupervised learning subsumes all kinds of machine learning where there is no known outputno teacher to instruct the learning algorithm in unsupervised learningthe learning algorithm is just shown the input data and asked to extract knowledge from this data types of unsupervised learning we will look into two kinds of unsupervised learning in this transformations of the dataset and clustering unsupervised transformations of dataset are algorithms that create new representation of the data which might be easier for humans or other machine learning algorithms to understand compared to the original representation of the data common application of unsupervised transformations is dimensionality reductionwhich takes high-dimensional representation of the dataconsisting of many featuresand finds new way to represent this data that summarizes the essential characteristics with fewer features common application for dimensionality reduction is reduction to two dimensions for visualization purposes another application for unsupervised transformations is finding the parts or components that "make upthe data an example of this is topic extraction on collections of text documents herethe task is to find the unknown topics that are talked about in each documentand to learn what topics appear in each document this can be useful for tracking the discussion of themes like electionsgun controlor pop stars on social media clustering algorithmson the other handpartition data into distinct groups of similar items consider the example of uploading photos to social media site to allow you