There are four types of machine learning algorithms: supervised, semi-supervised, unsupervised and reinforcement.
- Supervised learning
In supervised learning, the machine is taught by example. The operator provides the machine learning algorithm with a known dataset that includes desired inputs and outputs, and the algorithm must find a method to determine how to arrive at those inputs and outputs. The algorithm makes predictions and is corrected by the operator – and this process continues until the algorithm achieves a high level of accuracy/performance.
Under the umbrella of supervised learning fall: Classification, Regression and Forecasting.
- Classification: In classification tasks, the machine learning program must draw a conclusion from observed values and determine to
- what category new observations belong. For example, when filtering emails as ‘spam’ or ‘not spam’, the program must look at existing observational data and filter the emails accordingly.
- Regression: In regression tasks, the machine learning program must estimate – and understand – the relationships among variables.
- Forecasting: Forecasting is the process of making predictions about the future based on the past and present data, and is commonly used to analyze trends.
- Semi-supervised learning
Semi-supervised learning is similar to supervised learning, but instead uses both labelled and unlabeled data. Labelled data is essentially information that has meaningful tags so that the algorithm can understand the data, whilst unlabeled data lacks that information. By using this
combination, machine learning algorithms can learn to label unlabeled data.
- Unsupervised learning
Here, the machine learning algorithm studies data to identify patterns. There is no answer key or human operator to provide instruction. Instead, the machine determines the correlations and relationships by analyzing available data. In an unsupervised learning process, the machine learning algorithm is left to interpret large data sets and address that data accordingly. The algorithm tries to organize that data in some way to describe its structure.
Under the umbrella of unsupervised learning, fall:
- Clustering: Clustering involves grouping sets of similar data (based on defined criteria). It’s useful for segmenting data into several groups and performing analysis on each data set to find patterns.
- Dimension reduction: Dimension reduction reduces the number of variables being considered to find the exact information required.
- Reinforcement learning
Reinforcement learning focuses on regimented learning processes, where a machine learning algorithm is provided with a set of actions, parameters and end values. By defining the rules, the machine learning algorithm then tries to explore different options and possibilities, monitoring and evaluating each result to determine which one is optimal. Reinforcement learning teaches the machine trial and error. It learns from past experiences and begins to adapt its approach in response to the situation to achieve the best possible result.