Nowadays, we talk about artificial intelligence in a natural way, knowing what we mean by this concept. One of the most important disciplines in this field is machine learning , a technology that has become the basis for many actions that we carry out constantly without knowing it. In this article, we explore this concept in more depth, analyze how it works, and explain which machine learning applications are the most common.
What is machine learning ?
Machine learning can be defined as a branch of artificial intelligence (AI) that uses algorithms and data to collect, analyze and imitate certain patterns of human behavior and anticipate the development of certain tasks based on such learning.
In other words, machine learning allows certain machines, computers and devices in general to perform a series of specific tasks repeatedly and without having to be programmed to do so.
What is the origin of machine learning ?
The origin of this concept dates back to the mid-20th cameroon whatsapp data century, specifically, the 1950s. In 1950, Alan Turing created the Turing Test, with the aim of knowing whether a machine could pass itself off as a person to another human.
Two years later, in 1952, Arthur Samuel created the first algorithm capable of learning behaviour. In this case, acquiring patterns for playing checkers and improving after mistakes made in each game.
Deep learning vs machine learning
Deep learning , although sometimes used as a synonym for machine learning , is something different. This discipline, which can be considered a subgroup within machine learning , attempts to imitate the way in which the human brain processes information through artificial neural networks.
One of the key differences is that it requires almost no human intervention, unlike machine learning , which relies more on human intervention to achieve satisfactory results. This, in turn, makes deep learning capable of processing larger volumes of data , whereas the data set used in machine learning is smaller.
The evolution of artificial intelligence How do machines learn?
Intelligent learning by machines and devices is based on algorithms, and depending on the procedure they follow, we distinguish between three main categories:
Supervised learning
We refer to supervised learning when algorithms use different labels associated with data to establish relationships between them that allow them to make strategic decisions.
These labels are also key to acting in one way or another in the future when the device identifies them and associates them with a certain behavior pattern. A clear example is the ability of email servers to categorize emails as we have previously indicated.