Time spent for training and scoring can make or break a ML project. If an algorithm takes too long to train on currently available hardware, updating the model with new data and hyperparameter optimization will be painful, which may force you to cross that algorithm out from your candidate list. If an algorithm takes too long to score, then this is probably a problem in the production environment since your application may require fast inference times such as milliseconds or microseconds to get predictions. That's why it's important to learn the inner workings of ML algorithms, at least the common ones at first, to sense-check algorithms suitability.
For example, supervised learning algorithms learn the relationship between sets of examples and their associated labels the during training process, where each example consists of...
