There are so many different types of Machine Learning systems that it is useful to classify them in broad categories, based on the following criteria:
- Whether or not they are trained with human supervision (supervised, unsupervised, semisupervised, and reinforcement learning)
- Whether or not they can learn incrementally on the fly (online versus batch learning)
- Whether they work by simply comparing new data points to known data points, or instead by detecting patterns in the training data and building a predictive model, much like scientists do (instance-based versus model-based learning)
Machine Learning systems can be classified according to the amount and type of supervision they get during training. There are four major categories:
- supervised learning
- unsupervised learning
- semisupervised learning
- reinforcement learning
In supervised learning, the training set you feed to the algorithm includes the desired solutions, called labels.