por Have you heard about types of machine learning? If you haven’t already, you have to meet them now! He machine learning is taking more and more prominence in the world’s projects. Believe it or not, there are many elements that today have a level of machine learning or artificial intelligence. Therefore, it is very important to know how machine learning is shaping the world based on its 4 types. So, below we will talk to you about them.
4 types of machine learning
He machine learning or machine learning is one of the fields that participates in the development of artificial intelligence. This is responsible for learning from the data set of a program or service, to then improve the experience of a machine with predictions from previous experiences.
Since the machine learning helps solve problems like regressionclassification, forecasts and grouping, has to be divided into 4 types, which are: supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. So, below we will explain what the 4 types of machine learning consist of.
Supervised learning
First of all, the type of supervised learning is based on surveillance. A project with this technique must train the machines with a perfectly labeled database, which leads it to predict specific output data. In simple terms, with supervised learning we tell the machine what we want to learn and it must follow it to the letter.
For example, we can relate bootcamps and what time of year most people sign up for one of them. Thus, we would train a model that can decipher the relationship between the time of year and the number of enrollees.
In supervised learning there are two types of models, depending on the type of label:
Classification models are concerned with generating a discrete label, which will be within a group of more possible labels. Regression models that produce a real value, a single label.
Unsupervised learning
As its name reveals, this type of machine learning differs from the previous one in that it is not being supervised at any time. In this case, the machine will act on its own and will not need labels, but a group of data, to give results.
Thus, the machine should try to integrate or group all the results into labels by relationship. Meanwhile, The algorithm must find a way to make measurements and relationships between all the results found.
Two types of unsupervised learning appear:
Clustering or grouping is the type that allows recording the inherent groupings that emerge from putting all the results into analysis. On the other hand, the type association or association allows finding relationships regarding the variables of a large set of data.
Semi-supervised learning
The semi-supervised type of learning is a practice that is halfway between supervised and unsupervised learning. This way, only a minimal set of tags are used. Nevertheless, Most of them are unlabeled groups of data, as they increase costs, but are useful to meet objectives.
Although there is supervision over how the machine acts, it is not a job that is carried out throughout the work with the machine. While you will have to tag some results manually, others will be automatically proposed by the machine learning.
reinforcement learning
The last type we will talk to you about is the reinforcement learning type, a practice that is based on rewarding desired behaviors, while unwanted ones will be penalized. It is a process that is based on feedback, since the machine will learn from experiences, from development and performance.
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