What is Machine Learning: A Short Note on Supervised, Unsupervised, Semi-Supervised and…

For this family of models, the research needs to have at hand a dataset with some observations and the labels/classes of the observations. For example, the observations could be images of animals and the labels the name of the animal (e.g. cat, dog etc).

These models learn from the labeled dataset and then are used to predict future events. For the training procedure, the input is a known training data set with its corresponding labels, and the learning algorithm produces an inferred function to finally make predictions about some new unseen observations that one can give to the model. The model is able to provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct intended output (ground truth label) and find errors in order to modify itself accordingly (e.g. via back-propagation).

Supervisedmodels can be further grouped into regression and classification cases :

• Classification : A classification problem is when the output variable is a category e.g. “disease” / “no disease”.
• Regression : A regression problem is when the output variable is a real continuous value e.g. stock price prediction

Some examples of models that belong to this family are the following: SVC, LDA, SVR, regression, random forests etc.

2.2 Unsupervised machine learning algorithms/methods