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Machine Learning: How it Works, Benefits, Types

Technology / Date: 03-24-2025

The name already gives a lot away, doesn't it? This is the ability of man-made equipment to analyze data to automate the creation of analytical models.

It is, therefore, a branch of artificial intelligence , a broader concept, which concerns the ability of a machine to make decisions based on reasoning that resembles human thought.

In the case of Machine Learning, it is expected that these decisions made by the equipment are based on learning from data and identifying patterns with minimal (or no) human intervention .

Machine Learning was born from the idea that machines could learn to perform specific tasks even without having been programmed to do so.

The main goal of a developer in this area of ​​AI is to create software that, when exposed to new data, can adapt independently.

This data, added to previous calculations and sometimes subjected to repetition, produces reliable decisions and results.

Although some people see artificial intelligence and machine learning as trends that bring robots closer to what is most human and subjective in us, the basis of everything is still the Exact Sciences .

What allows a machine to have something similar to intelligence are algorithms, and we will talk more about them below.

How does Machine Learning work?

There are approaches to artificial intelligence that study brain structures, that is, the functioning model of neurons, to create intelligent machines.

But it is not expected, at least not in the next few decades, that machines will be able to achieve the same result as that produced by millions of years of natural selection.

In AI, Machine Learning, and all of computer science, algorithms are the foundation of everything .

They are sequences of rules and operations that, when applied to a set of data, produce a certain result.

In order for machines to learn, algorithms are subjected to certain methods, which are divided into two approaches.

The first is supervised , in which the algorithm learns because it receives data that contains the correct answer.

In the unsupervised approach , on the other hand, the data the algorithm receives is not labeled, so the effects of the variables are unpredictable.

This second approach, therefore, is more complex and advanced, because in it the machine itself finds the desired patterns and improves its filters as it is used.

What are the benefits of using Machine Learning?

Now that you know a little more about what Machine Learning is, how it works and its importance, it's time to understand what this technology can add in practice.

We have listed some benefits so that you can have a clear idea of ​​the advantages that machine learning can bring to a company's production routine .

Check it out:

Continuous learning and improvement

This advantage has everything to do with a concept called interactivity (that's right, without the “n”), which means improving based on repetition or a history of attempts.

In other words, it is the ability to learn autonomously and provide responses that become more assertive over time.

Furthermore, Machine Learning, whenever placed in contact with new variables, reprograms itself, updating the configurations according to the newly arrived data .

Therefore, technology is constantly evolving , as it can recognize patterns based on results it has found in the past, and refine the interpretation, without the need for new human interference.

Unlimited data processing

With the amount of data generated today, it is humanly impossible to process everything without the support of technology.

Big Data uses Artificial Intelligence and its respective tools, such as Machine Learning, to capture, integrate, analyze and interpret this information.

Thanks to this help, it is possible to read content in different sizes and formats and much more quickly.

It is from this processing that companies can extract insights to improve the user experience .

After all, automating data management allows information, such as consumer histories and habits, to be processed and given a more assertive interpretation .

Efficiency

Operational efficiency is one of the main goals of a company. After all, what business doesn't want to reduce costs and, in the process, increase revenue ?

Because Machine Learning can help in this important mission.

By automating certain bureaucratic tasks, it is possible to increase the level of assertiveness , since human error will almost be non-existent.

At the same time, you can reallocate real-world employees to intellectual activities that involve greater decision-making power .

A practical example of how technology can improve a company's efficiency is in predictive maintenance .

When you can anticipate certain problems, rather than fixing the consequences after they have already occurred, you save time and money.

With Big Data, Artificial Intelligence and Machine Learning, problems with software updates, machinery recalls due to the model or year of manufacture can be resolved in advance .

Even inconsistencies in production, shown by data from some sensors, can be avoided.

Speed

In addition to processing an unlimited amount of data , AI technologies involving Machine Learning go further.

They can do this and monitor the evolution of information in real time or very close to it.

The speed at which these technologies operate is another benefit, as companies can extract important reports and, moments later, use that data to produce personalized content on time.

Adaptability

A company's strategies and objectives need to be flexible to adapt to the different variables that may arise.

Therefore, it is very important to have technologies that allow data to be processed in real time.

If a problem occurs or a plan proves to be too optimistic, it is possible to adapt to a situation that is closer to reality.

Sometimes, it is not a question of internal issues, but of the occurrence of external factors, such as an economic crisis or a market slowdown.

All of this can force a readjustment , which can only be done with the necessary speed and assertiveness by those who know how to interpret the indicators.

Types of Machine Learning

Just like humans, machines also have different ways of learning .

From now on we will talk about the most well-known types of Machine Learning.

Supervised learning

It is a model in which the machine receives a set of data with labels, divided into different classifications .

Supervised learning is often used to anticipate results in which you already have an idea of ​​the possible outcomes , mainly yes or no cases.

To check whether a transaction made by credit card is the result of fraud or not, for example.

This is possible thanks to the classification and regression techniques applied in this type of teaching method.

• Classification: can categorize data based on learned labels. For example, an algorithm already knows what types of primary and secondary colors there are, so when an element appears in red, it will be placed among the primary colors.
• Regression: used to predict continuous values, i.e. variables that tend to repeat themselves within a logic over a given time interval. For example, calculating returns for investments with pre-fixed rates.

Unsupervised learning

It is a model in which the machine receives a series of data that have no labels and, therefore, there is no prospect of predicting the final result.

In these cases, not even humans know what information can be extracted.

The idea behind this type of learning is to recognize certain patterns and, from there, find logic in the data.

In this sense, unsupervised learning basically uses three techniques to identify these possible relationships between information:

• Clustering : searches for similarities between data and divides them into groups as soon as these similarities are found. It can be used, for example, to segment your target audience and facilitate the creation of personas.
• Dimension reduction: helps eliminate random data, making only the most consistent variables prevail. It can be used in risk management plans, for example, by reducing less likely outcomes.

Semi-supervised learning

As the name suggests, it is a model that works as a hybrid of the previous two.

Typically, it is used when there is a large volume of data, but only part of it has labels , which is the condition that enables fully supervised learning.

In this case, the machine and its algorithms learn from both supervised and unsupervised data.

In practical terms, this method can be used to perform facial recognition of a person using a webcam or smartphone camera, among other applications.

Reinforcement learning

It is a method in which the machine learns through trial and error .

In a way, it resembles the reward system used in child psychology to reward children who perform a desired behavior.

In the case of machine learning, this model somewhat disregards the value of the data (labeled or unlabeled), and places more value on the environment .

In this model, there are always three variables: the agent (the machine), the environment (the place where the agent acts) and the actions (the agent's activities).

Think of the problem as a big puzzle game where you have to match all the pieces correctly.

Therefore, for each piece combined, the machine makes a stitch, and for each wrong combination, it loses.

In other words, the person learns by reinforcing an action, whether positive (success) or negative (mistake), in pursuit of the final objective, which is to find the best strategy in the shortest time.