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There are so many algorithms available and it can feel overwhelming when algorithm names are thrown around and you are expected to just know what they are and where they fit.In this post I want to give you two ways to think about and categorize the algorithms you may come across in the field.
Both approaches are useful, but we will focus in on the grouping of algorithms by similarity and go on a tour of a variety of different algorithm types. After reading this post, you will have a much better understanding of the most popular machine learning algorithms for supervised learning and how they are related.
A cool example of an ensemble of lines of best fit. Weak members are grey, the combined prediction is red. Plot from Wikipedia, licensed under public domain.