So far, we've discussed three major classes of machine learning algorithms. However, to solve an ML problem, we'll need a system in which the ML algorithm is only part of it. The most important aspects of such a system are as follows:
- Learner: This is algorithm is used with its learning philosophy. The choice of this algorithm is determined by the problem we're trying to solve, since different problems can be better suited for certain machine learning algorithms.
- Training data: This is the raw dataset that we are interested in. This can be labeled or unlabeled. It's important to have enough sample data for the learner to understand the structure of the problem.
- Representation: This is how we express the data ...