Types of Machine Learning

Machine learning algorithms can be divided into three board categories:

Supervised learning: 

The computer is presented with example inputs and their desired outputs, and the goal is to learn a general rule that maps inputs to outputs. An example is an email spam filter.

Unsupervised learning: 

No labels are given to the learning algorithm, leaving it on its own to find structure in its input (discovering hidden patterns in data). For example, imagine having data about all cars and their buyers. The system can find patterns and identify that, for example, people in the suburbs prefer SUVs with petrol engines, but people who live near to downtown, prefer smaller electrical cars. Knowing this can help the system can find patterns and identify that for example people in the suburbs prefer SUVs with petrol engines, but people who live near to downtown, prefer smaller electrical cars. Knowing this can help the system predict who will buy which car.

                      Machine learning algorithms can be divided into three board categories:

Reinforcement learning: 

A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). The program is provided feedback in terms of rewards and punishments as it navigates its problem space.

Another categorization of machine learning tasks arises when considering the desired output.

In classification (typically in supervised learning) inputs are divided into two or more classes. Spam filtering is an example of classification, where the inputs are emails and the classes are spam and not spam

In regression, also a supervised problem, we predict continuously valued outputs. For example, predicting house price or stock prices.

In clustering, a set of inputs is to be divided into groups. Unlike in classification, the groups are not known beforehand, making this typically an unsupervised task. An example is customer segmentation.

Density estimation finds the distribution of inputs in some space. For example, having diabetes test results of a specific number of people, we can estimate the distribution for the whole population.

Dimensionality reduction simplifies inputs by mapping them into a lower dimensional space. Topic modelling is a related problem where a program is given a list of human language documents and is tasked to find out which documents cover similar topics.