We have seen a basic kit of machine learning tools and a few indications of their uses on simple datasets. What you may be beginning to wonder is how these tools can be applied to real-world problems. There is considerable overlap between each of the libraries we have discussed. Many perform the same task, but add or perform the same function in a different way. Choosing which library to use for each problem is not necessarily a definitive decision. There is no best library; there is only the preferred library, and this varies from person to person, and of course, to the specifics of the application.
In the next chapter, we will look at one of the most important, and often overlooked, aspects of machine learning, that is, data.