Many problems have well-defined solutions that are obvious once the problem has been defined.

A set of logical steps that we can follow to calculate an exact outcome.

For example, you know what operation to use given a specific arithmetic task such as addition or subtraction.

In linear algebra, there are a suite of methods that you can use to factorize a matrix, depending on if the properties of your matrix are square, rectangular, contain real or imaginary values, and so on.

We can stretch this more broadly to software engineering, where there are problems that turn up again and again that can be solved with a pattern of design that is known to work well, regardless of the specifics of your application. Such as the visitor pattern for performing an operation on each item in a list.

Some problems in applied machine learning are well defined and have an analytical solution.

For example, the method for transforming a categorical variable into a one hot encoding is simple, repeatable and (practically) always the same methodology regardless of the number of integer values in the set.

Unfortunately, most of the problems that we care about solving in machine learning do not have analytical solutions.