- Algorithms in this branch of AI receive training data to find patterns and create models that can be applied to new data.
Before learning what machine learning algorithms are, let’s briefly review what each of these concepts means separately.
What are algorithms?
The RAE defines algorithms as ordered and finite sets of operations that allow a solution to a problem to be found. In other words, they are limited and ordered instructions for achieving a goal step by step.
The instructions to be followed must be ordered in a finite series of instructions in order to reach the relevant solution, with an input and an output for these instructions.
Precision, order, specificity, as well as being finite and defined, are some of the characteristic features of algorithms.
What is machine learning?
When asked what machine learning is, it could be summarized as a branch of artificial intelligence understood as the ability of a program to recognize patterns in large volumes of data, which allows it to make predictions.
With information processing, machines can work autonomously by acquiring knowledge on their own, without having to be programmed in advance, which allows the program to learn, identify patterns, and generate predictions.
Machine learning algorithms
Now that we know the definitions of both terms, we can summarize that machine learning algorithms are sets of instructions that allow machines to learn data patterns with which to make predictions or decisions without having been specifically programmed, and which also improve with experience.
Let’s take a closer look at the main types of algorithms used in machine learning:
- Regression. These types of algorithms are used to model the relationship between different variables by using an error measure that will tend to be minimized in order to make the most accurate predictions possible. They are especially used in statistical analysis.
- Bayesian. The name comes from Bayes’ theorem, a mathematical formula that calculates the probabilities of an event occurring if another has already occurred. These algorithms are used for regression and classification.
- Instance-based. In this case, a model is created based on a database, and new data is added by comparing its similarity to existing samples to find the best match and make the prediction. They are also known as winner-takes-all algorithms.
- Decision tree. In this case, decision-making is modeled based on the current values of the data attributes and is mainly used to classify information, branching and modeling the possible paths to take and, with that, the probability of occurrence to improve its accuracy.
- Clustering. With the creation of central points and hierarchies to differentiate groups and identify common characteristics, used to group existing data whose common characteristics are unknown and/or that are to be discovered.
- Neural networks. Inspired by the biological functions of neural networks, these algorithms are used to detect patterns and are used for classification and regression problems, although they have the potential to solve problems of various kinds.
Regardless of the type, algorithms receive training data to find patterns and create models (the results) that can be applied to new data to advance predictions or develop classifications, with the aim of improving accuracy as they are exposed to more data.
