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What is machine learning? Ask all your questions here!

Machine learning and the various tools of artificial intelligence and data analysis have as differentials the ability to hand over much of the work to the machines themselves to do.


What if we could transfer repetitive and unproductive tasks to an intelligent computer system? Then we could focus on more challenging work and add value to the business. The problem is that such a solution does not exist. Right?

Definitely not! Machine learning and the various artificial intelligence and data analysis tools have the distinction of being able to hand over most of the work to the machines themselves.

Thinking of talking a little more about the concept, its applications and its many benefits, I prepared a particular post on the subject. The subject has been quite discussed lately, even because of the General Law of Data Protection. Enjoy your reading!

After all, what is machine learning?

Also known as machine learning, the technology can be described as the ability to teach computers to think and act on their own. In this way, software can learn, through prescriptive data analysis, to come up with precise answers to certain computational problems.

The concept may seem rather far from everyday application, right? Nothing could be further from the truth. This technology has already become quite common in people's routines - and not only for those who work with digital solutions.

So when you do a search on Google or accept a suggestion for a new movie on Netflix, you are directly using a machine learning application. That's because the technology makes it easy for the search engine to identify better results and even point out TV shows that may be of interest to you based on your browsing history.

The main goal of this technology is to enable machines to make decisions without human interference. This is made possible by algorithms (logical sequences of instructions passed to the computer), which enable the computer system to identify patterns and point out appropriate solutions.

Unlike an ordinary program, a machine learning system can learn while performing the functions it is programmed to do. This characteristic makes it much more efficient than traditional software.

Connection with artificial intelligence

Often, when reading about technology, people come across concepts such as machine learning being used as synonyms. This conception is not entirely wrong, but it is simplistic: we can say that machine learning is one of the manifestations of AI.

In this sense, it joins other much-discussed technologies, such as Big Data, to enable practical applications of artificial intelligence.

Difference from deep learning

Similarly, deep learning is a way of putting artificial intelligence into action. While machine learning operates exclusively on structured data and requires the organization of that information, the sophisticated deep learning technology and self-learning capabilities themselves result in higher accuracy and faster processing.

So we can say that machine learning laid the foundation for deep learning to evolve, inheriting the main characteristics of the traditional machine learning model and developing new features.

An interesting and recurring application of deep learning algorithms is optimized image recognition.

Why invest in machine learning?

Now that we know the concept and its relationship with artificial intelligence and deep learning, it is time to learn about the advantages of investing in the technology.

Cause knowledge for decision making

With an increasingly tight market and the ease with which customers find new options, the most efficient managers must find differentiated strategies to identify new business opportunities.

Without this care, building customer loyalty and winning new customers becomes a difficult job. However, to develop strategies that enable more profitability, it is necessary to have accurate information to work with. That's where machine learning comes in.

This is because every company generates a high load of data, especially with the internet driving most businesses. With machine learning tools and data analysis, it will be possible to map the organization's digital history, establish consumption patterns, and generate new practical loyalty actions.

Machine learning studies information related to the business with the objective of tracing consumption habits. It is possible, for example, to find out which products sell best in a given period and anticipate this demand for the following year. The tool goes through the purchase history and offers valuable insights for the company.

Optimized use of resources

An important way to measure the success of an enterprise is to study how it directly allocates its physical and financial resources. A company that fails in this management tends to suffer continuous losses and show low overall productivity.

However, mapping out all the processes manually to identify flaws and pinpoint bottlenecks is a very slow and stressful procedure for employees. You have to appoint leaders and move them from their usual roles, for example.

As if that were not enough, every employee, no matter how talented, can get complicated when dealing with too much information. By using machine learning solutions and automating some processes, the company benefits by handing over purely manual jobs to machines.

In addition to stimulating employees, who then dedicate themselves more to the company's core activities, this digital solution also reduces rework, since it eliminates the risks associated with human error.

Increased profitability

Let's suppose that your company manages to sell a good amount of products every year. However, even though the numbers cover the losses, it has not grown significantly. This is because daily activities make it difficult to find time to identify new opportunities.

In this context, machine learning emerges as a tool capable of pointing new paths for the organization. This is because the technology helps the company to be part of a data driven culture - that is, one that uses Big Data and machine learning to find new ways to innovate.

Technology can analyze public databases such as government studies, social media, and information found on the Internet to identify trends. This is especially important for finding gaps in the market and exploiting them to launch products accordingly, generating more profitability for the business.

For whom is machine learning indicated?

We know the benefits, now is the time to show the various industries that have invested in machine learning and artificial intelligence.

Financial Institutions

Banks, fintechs, and other financial and credit analysis institutions are investing in machine learning to extract more information about their customers and study transaction histories.

This is because this technology provides the ability to predict market risks (such as loans), ensures fast fraud detection, and assists in compliance management - financial compliance in accordance with applicable legislation.


For federal and municipal governments and institutions, working with data is essential to quantify information from thousands of citizens and make better informed decisions.

In fact, the Brazilian Federal Revenue Service was one of the winners of the "100+ Innovators in the Use of IT" award, promoted by the IT Mídia institute. This is because the institution became known for its use of data mining and machine learning tools to improve its tax collection service.

Healthcare Companies

In a sector as sensitive as healthcare, it was already expected that artificial intelligence would provide tools to improve diagnoses and provide more safety in treatments. The main applications have to do with risk triage, medical data analysis, and patient status monitoring.

Management consultancy Accenture estimates that spending on artificial intelligence and machine learning in the industry will reach the $6.6 billion mark by 2021.

Marketing and Sales

The marketing area also reaps good rewards from the application of machine learning tools. Here, technology is used to get to know consumers better, identify their perception of their relationship with a brand, and develop precise strategies based on the information extracted.

In addition, marketing itself has been automated, providing regular customer contact without human error.

Oil and gas industry

In the fuel market, data collection has a variety of objectives. It ranges from predicting risks in certain investments to compliance actions. The machines themselves have become capable of recommending the wells that most need attention, and automating related tasks.

Transportation and Logistics

With the right machine learning tools, it has become easier to increase operational efficiency in transportation companies, and to apply these benefits to the logistics of other companies. The solution can predict seasonal demands and improve the distribution and stocking of products.

What are some practical applications of the technology?

Okay, we know the main industries that have benefited from machine learning applications. But what are the specific applications? It's time to introduce them.

Search Engines

Life before search engines was difficult. A student who needed content to finish a college paper, for example, had to buy books to inform himself or consult the libraries in his area.

Since small towns often do not even have a library, residents of these areas had even greater difficulties. Also, there was not the famous "ctrl + l" combination to find the chosen terms and not have to read whole pages uselessly.

Are you grateful for the ease of search engines that help you with every search on websites and digital documents? Well, thank machine learning. It is what powers tools like Google, for example.

Optimized spam detection

Who has never opened their email and found themselves with a dozen or so new messages, but few really important communications? A technology created to facilitate communication, e-mail has also become a target for spam.

This hindered corporate relations, as workers lost time reading automatic messages. In the rush to delete everything, it was even common for really important e-mails to go to the recycle garbage can together.

However, machine learning and artificial intelligence tools now identify messages from dubious sources and automatically direct them to the folder the user wants. No more wasting time with advertisements and extortion attempts, after all - and a corporate antivirus can also help identify digital threats in emails.

Biometric reading

We often have the feeling that a mere password is not enough to grant access to certain environments. There must be a more secure way to ensure that only authorized people can enter certain places, right?

In fact, it exists: it is biometric recognition, a technology that also relies on machine learning. In this way, access to certain environments can only occur by this type of reading, or even by voice. This is especially true for companies that have a lot of valuable information about their customers. No matter how careful IT professionals are, they have to guard against fraud.

Automation of processes and tasks

Process robotization and service automation have many benefits for companies in different segments. By using this kind of capability, companies reduce expenses related to rework and eliminate human errors.

This is because the machines are programmed to perform tasks with the level of excellence required by the company's standard. To facilitate automation, machine learning solutions are indispensable. It is through technology that services can be automated.

It is interesting to note that the future holds much promise, especially when it comes to machine automation: autonomous cars are already being tested all over the world and the trend is that a good number will soon be on the streets.

How to create good machine learning systems?

Briefly, a robust machine learning system has a few essential components:

  • algorithms;
  • automated processes;
  • scalability of the tools;
  • optimized data analysis capability;
  • predictive modeling.

This is a challenging set for those who are not yet very accustomed to dealing with advanced technology. In addition, you have to handle the data and make sure that it has the necessary quality.

Therefore, the best thing to do is to rely on specialized partners to implement machine learning appropriately in your company. With professionals who study the subject daily, the application will be facilitated, saving a lot of time.

Do you understand what machine learning is and how this technology has revolutionized companies from different segments? We are in a really special moment, as every business has access to cutting-edge tools. This facilitates the ability to automate processes and develop new strategies based on data.

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