Depending on the nature of the learning feedback available, the machine learning process is typically classified into three broad categories: supervised learning, unsupervised learning, and reinforcement learning – see figure 1. A predictive model based on supervised learning algorithms can make predictions based on a labelled dataset that map inputs to outputs aligning with the real world.
For example, a dataset for spam filtering usually contains spam messages as well as not-spam messages. Therefore, we could know which messages in the training set are spam and which are ham. Nevertheless, we might have the opportunity to use this information to train our model in order to classify new unseen messages:
Figure 1: Machine learning tasks (containing a few algorithms only)
The following figure shows the schematic diagram of supervised learning. After the algorithm has found the required patterns, those patterns can be used to make predictions for unlabeled...