Have you ever imagined a non-fictional scenario where machines could be capable of learning? As futuristic as it sounds, this resource exists, has a name and even a last name: Machine Learning (ML) - a branch of computer science, also known as machine learning.

Seen as a subset of artificial intelligence, the field develops devices capable of understanding and executing tasks. Want to find out how this concept works in practice and what its advantages and disadvantages are in the business context? Then you're in the right place!

Follow along with the reading and check out the article that the CodeBlog team has prepared for you.

Machine learning: what is it?

ML is a concept that encompasses the Internet of Things and Artificial Intelligence to develop and improve machines capable of learning on their own, requiring only a large volume of data.
In this way, the devices detect patterns and connect information, using resources like Big Data and sophisticated algorithms that allow tasks to be executed in a completely autonomous and independent manner.

Machine learning: how does it work?

In ML, the received data is analyzed by algorithms, which generate specific answers and predictions, making software and applications smarter, precisely because of the predictions.
This computational model operates through artificial neural networks, based on the functioning of the human brain (which also learns based on pattern recognition).
Although it is necessary for programs to present specific functions to support the technology, the most interesting thing about machine learning is the fact that the software does not need to be programmed frequently to learn.

What are the advantages of machine learning?

Now that you know what ML is and how it works, discover below how the application of this technology can boost the results of companies and entities.

Learning with data

Data is the future of technology, and the good news is that Machine Learning is capable of concentrating an unimaginable amount of complex information, analyzing it, and even learning from it.
In practice, the resource not only multiplies the possibilities of the internal processes of a company or institution, but also creates algorithms capable of predicting future scenarios with more assertiveness.
Generally speaking, with the application of Machine Learning, it becomes much easier to retrieve histories and study them.
This allows, for example, analyzing whether a trend is fleeting or lasting, the success or failure of a launch, and even identifying the best strategies to meet market demands.

More productivity

ML is capable of automating, and even replacing, various internal processes and tasks performed by humans, ensuring greater speed, assertiveness, and reduction of errors.
With this, a company gains more agility in identifying and resolving problems, studying reports, assistance for decision-making, and various other benefits that would not be possible without the aid of computer science.
With machine learning, a team can automate repetitive and bureaucratic routines and focus on strategic actions.
Thus, corporate productivity is visibly enhanced, without the need to increase the number of employees.

Cost reduction

At the same time that Machine Learning contributes to increasing a company's productivity, it also replaces some outsourced services with more practical, modern, and automated solutions. We present as an example ML systems that interpret contract clauses and convert legal language into terms that are easier to understand.
In these situations, the software itself helps in identifying missing items in the contract or erroneous clauses, such as fees or penalties that were not previously agreed upon.

Disadvantages of Machine learning

Despite the advantages, the application of Machine Learning algorithms also presents some inconveniences that must be mentioned. Check them out in the next topics:

Data dependency

Machine Learning depends, above all, on the structure, quality, and availability of data, which means that if an application does not allow for proper collection and analysis of this information, it will be almost impossible to use the resource effectively.

Generalization

No matter how correctly Machine Learning algorithms operate with the available data, when new information is applied, there may be a drop in performance. In general, the issue of generalization demands care and attention during the development of the model, as well as the appropriate definition of training and testing sets.

Processing time

It is also important to highlight that, depending on the complexity of the context and the type of algorithm used, model training may require a long processing period and high consumption of computing resources.
As an example of this, we can mention deep learning models, which use artificial neural networks with several layers, requiring high operational capacity, in addition to a period that can extend past hours, days, or even weeks, until reaching completion.

Conclusion

We hope this post has helped boost your understanding of what Machine Learning is and what its advantages and disadvantages are.
This is a broad concept involving countless debates, scenarios, and discussions.
Soon, we will bring new articles about this revolutionary resource here on the CodeBlog. Keep an eye on our communication channels and follow all the tips, news, and updates from the technological universe.

A big hug and see you in the next post!