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Artificial Intelligence

Supervised Learning: What It Is and How to Use It in Your Work

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supervised learning

Do you know what supervised learning?

In one of Disney's most celebrated short films, Mickey is a magician's apprentice who enchants brooms and mops to do his cleaning for him. But when he takes a nap and leaves the objects to their own devices, they draw water from the well non-stop and flood the castle.

Mickey wakes up and, on being rescued by his master, learns an important lesson: you have to start a process with an end goal in mind.

In a way, supervised learning works in the same way. For it to deliver the expected result, you need a vision of what you want to achieve. In this article we'll teach you what supervised learning and how you can use this tool in your work routine. Shall we get started?

What is Supervised Learning

O supervised learning - in Portuguese, supervised learning - is a category of machine learningin other words, it is one of the layers of Artificial Intelligence (AI).

Supervised learning is a way of training AI algorithms to process data according to the result the user wants to obtain.

To help you visualize this conceptimagine the following situation: you've been given a jigsaw puzzle with five thousand pieces. To assemble it, you decide to sort the pieces by color and shape.

With the pieces categorized, now it's time to look at the image you need to assemble and determine how you're going to achieve this. Will you start with the edges? Or perhaps the simplest part of the image? The choice is yours. As long as in the end the image is assembled correctly.

O supervised learning is like putting this puzzle together. You have a set of data at the start of the process and you know what product you need the computer to be able to deliver.

The way the machine will work to achieve this result, and the adjustments it needs to make to the AI model, is through learning. 

Supervised Learning and Unsupervised Learning

O unsupervised learning - or unsupervised learning - works without any idea of what the end product should be. Therefore, the machine will choose the stages of the process and how it will present what it has learned.

This model has advantages, especially for orchestrating a more flexible process. In this way, the solutions presented by AI can be more varied and meet other needs. 

How Supervised Learning works

Let's go back to the puzzle example. 

To start supervised learningit is necessary to select a database, i.e. the set of pieces to be assembled. Once this selection has been made, the next step is to determine the final result, which image the pieces should assemble.

With these choices made, the AI will carry out one, or two, tasks.

1. Categorize

Just as puzzle pieces have colors and shapes, data also has categories in which it can be organized. 

No supervised learning these categories may or may not be determined. If they aren't, the AI will test ways of organizing the data and offering the user a way of visualizing the patterns and drawing conclusions.

2. Regress 

With the categorization done, the AI can now analyze the historical pattern and determine what the relationships are between the data and its variables. During this supervised learning, the computer will understand what caused the changes and make projections for the future.

Where to Apply Supervised Learning 

The supervised learning models have applications in various areas of technology. For marketing, some are more relevant, so we'll focus on them.

Editing images and videos 

Algorithms for supervised learning are used to analyze images and videos to determine their characteristics. For example: colors, lighting and style.

As soon as the AI manages to isolate these points, it can:

  • Adding elements to an image (inpainting);
  • Enlarging an image fragment (outpainting);
  • Change the characteristics of present elements;
  • Edit colors, lights and shadows automatically.

Making Predictions and Determining Trends

By analyzing the results obtained previously through supervised learning, it is possible to anticipate trends. For example, detecting when sales are highest, creating a seasonal calendar for advertising investments.

Managers can make decisions that will impact the business, whether it's avoiding a problem or optimizing a growth measure. This ability to predict what a company will look like in a few days, months or years is a valuable tool. 

Managing the Relationship with Your Consumers

By using supervised learningit is possible to evaluate not only the content of consumers' messages, but also the intent of the message and the sentiment expressed.

With this information in hand, Customer Success (CS) managers can act to reverse detracting customers and strengthen relationships with promoters. 

Another point of action is to detect which negative messages could damage the brand's reputation and should be dealt with at that time. Instead of remedying a problem, the company can act proactively to get around the situation before it gets worse. 

The Problem of Supervised Learning 

Emerging technologies still need adjustments to work properly. It is therefore to be expected that supervised learning has some room for improvement.

The most relevant of these is the issue of time. Training AI models is a process that consumes a lot of time and consistent effort. Therefore, for those who need quick solutions, supervised supervised learning may not be the most suitable option.

Conclusion

Just like putting together a jigsaw puzzle, training AI models can be a slow process, but in the end it can produce innovative solutions.

And now that you know what supervised learningyou know another category of AI that can be used in your business, whether it's to edit images, make predictions or manage your consumer base. This and other technologies are changing the way work is done in companies. To stay on top of everything, follow the articles here on the Pareto blog!

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