What are the benefits of using Machine Learning?

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

We have separated some benefits so that you can have a clear idea of ​​what machine learning can bring benefits to the productive routine of a company .

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Learning and continuous improvement

This advantage has everything to do with a concept called iterability (just like that, without the “n”), which means improving from repetition or the history of attempts.

That is, it is the ability to learn autonomously and deliver answers that are more assertive over time.

In addition, Machine Learning, whenever brought into contact with new variables, reprograms itself, updating the settings according to the newly arrived data.

Therefore, the technology is constantly evolving , as it is able to recognize patterns based on the results it has already 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 faster.

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 gain 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, at the same time, increase revenues ?

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 flesh-and-blood collaborators 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 are able to anticipate certain problems, rather than repairing the consequences after they have already occurred, you save time and money.

With Big Data, Artificial Intelligence and Machine Learning, issues with software updates, machinery recalls due to 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 that involve Machine Learning go further.

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

The speed at which these technologies operate is an added benefit, as companies can extract key reports and then instantly use that data to produce personalized content on time.

Adaptability

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

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

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

Sometimes, it’s not about internal issues, but the intercurrence of external factors, such as an economic crisis or a market downturn.

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

What are the most popular types of Machine Learning?

As with humans, machines also have different ways of learning.

From now on, we will talk about the most popular 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 widely used to anticipate results in which one already has an idea of ​​what the possibilities of outcome are, mainly yes or no cases.

To verify if 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 are, so when an element appears in red, it will be placed between the primary colors.
  • Regression: it serves to predict continuous values, that is, variables that tend to repeat themselves within a logic during a certain time interval. For example, calculating returns for fixed-rate investments.

unsupervised learning

It is a model in which the machine receives a series of data that do not have 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 in this type of learning is precisely to recognize certain patterns and, based on them, find a logic between the data.

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

  • Clustering : Looks for similarity between the data and divides them into groups as soon as it finds these similarities. It can be used, for example, to segment your target audience and favor the creation of personas
  • Association: combines two or more pieces of data, finding a sequence and identifying patterns. This is the case with content recommendations or shopping suggestions, among others.
  • Dimension reduction: helps to 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.

It is normally used when there is a large volume of data, but only part of it has labels , which is the condition that allows 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 the child who performs a desired behavior.

In the case of machine learning, this model slightly disregards the value of data (labeled or unlabeled), and values ​​the environment more .

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

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

Therefore, for each matched piece, the machine scores a point, and for each wrong association, it loses.

That is, it learns by reinforcing an action, whether positive (success) or negative (mistake), in search of the ultimate goal, which is to find the best strategy in the shortest time.

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