In this chapter, we have covered two common techniques in unsupervised machine learning. Both are often used by data scientists for exploratory analysis, but can also form part of a data processing pipeline in a production system. You have learned how to train a clustering algorithm to divide data automatically into groups. This technique might be used to categorize newly registered customers on an e-commerce website, so that they can be served with personalized information. We also introduced principal component analysis as a means of compressing data, in other words, reducing its dimensionality. This may be used as a preprocessing step before running a supervised learning technique in order to reduce the size of the dataset.
In both cases, it is possible to make use of the gonum and goml libraries to build efficient implementations in Go with minimal code.
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