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All You Need to Know About Machine Learning

Machine Learning (ML) is a popular topic, ever since Artificial Intelligence (AI) has made some vital steps forward in development. It can also be an intimidating topic if you’re new to the concept (or you haven’t watched the Terminator series!).

In this article, we’re covering the machine learning definition and explaining the basics in this domain. So, keep reading to discover machine learning basics and other information that will help you have a better understanding of the entire phenomenon.

What is Machine Learning?

Machine Learning defines an ensemble of Artificial Intelligence (AI) systems that are programmed to allow computers (or any other machine that can connect to the Internet) to improve at a certain task without interventions from humans. In other words, Machine Learning is the process we’re going through every day; we get new data daily and always try to make sense of it to be better and improve our lives.

Machine Learning Example

Assume your card is stolen, and the thief makes an online transaction from a different country or a merchant you never used before. Let’s say it makes a deposit to claim an online casino bonus, but you never used the card for such a purpose before.

In most cases, you will get a phone call, text, or email from the bank asking if you’re the one doing the transaction. Through machine learning, the bank gets familiar with your card purchasing habits, and it was able to detect this suspicious transaction.

machine-learningMachine Learning Types

As you’ve probably expected, there isn’t just one type of Machine Learning. Depending on how the system is set, there are three main types, and Machine Learning algorithms work differently in each situation.

Supervised Learning

This first machine learning classification refers to a situation in which a human feeds the algorithm with labeled data, and uses it as a knowledge base to determine how to resolve similar problems in the future. It is offering algorithm problems and the answers to them, so it can learn how to solve such problems to be prepared for similar operations in the future. This is the type of Machine Learning we used in the example with the bank. The machine was fed an online purchase history and habits, and it was able to immediately determine what did not fit the pattern. 

Unsupervised Learning

In this Machine Learning type, you give the algorithm a problem, but without labeled data or knowledge regarding the answer. It has to use the available data to find the best solution for the problem at hand. By driving insights from the available data, the machine creates various scenarios and tests if the problem can be solved with any of them. It repeats the process until a convenient solution appears. This type of Machine Learning is often used in banks to determine if their customers are available for various credit options, using bulk data.

Reinforcement Learning

In this Machine Learning situation, the algorithm simply uses its own experiences to learn. Through a trial-and-error system, the machine gets closer to the solution by testing various scenarios.

It is very similar to how humans and other species learn at a young age. Remember the first time you touched a hot surface? The burning sensation and pain taught you that’s not a good idea, and you never did it again. In our case, by using a reward and penalty system, the system is taught to get closer to the solution. Its goal is to maximize the reward, and, in doing so, various solutions are found; an excellent way to boost the machine’s creativity.

The best example of reinforcement learning can be observed in self-driving cars. Through a series of decisions, the system learns how to maintain the lane, turn when needed, and avoid crashing into other cars. Of course, everything is done through trial-and-error, which is why we don’t have more of a widespread use of self-driving cars just yet. It’s a Machine Learning process that still needs some refining before it can be applied more broadly in the world.

Conclusion

Machine Learning has lots of potential, and it can improve numerous aspects of our everyday life. However, there are many individuals out there fearing that, through Machine Learning, their jobs will become obsolete. However, that’s not the purpose of Machine Learning and AI altogether. The idea is to let computers take over the repetitive and time-consuming tasks, so that humans use their native creativity and instinct for more important work.

Scott Carrion
Scott Carrion
Scott Carrion is a freelance writer and analyst focusing on business and marketing. His Master’s degree in Business research from Curtis L. Carlson School of Management has given him a broad base from which to approach many topics. He works closely with B2B and B2C companies providing useful and engaging content that can convert viewers into customers. His favorite way to spend holidays and vacations - visiting new countries.

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