What is machine learning?

03.11.2020 Angelika Siczek
machine learning

Artificial intelligence (AI – artificial intelligence) has been appearing in various spheres of business for several years. It is particularly popular in the field of marketing. It is there that the creation of intelligent behavior models as well as programs and systems simulating this type of behavior brings the most benefits. In marketing, the achievements of AI, i.e. machine learning, are eagerly used. It is thanks to it that marketers can significantly improve their work. What exactly is machine learning and why is it worth using it in your own business? Find out from our article!

Machine learning – definition

The concept of machine learning has long existed in science. Arthur Samuel, who introduced the term around 1959, is considered as its creator. It originally defined the ability of computers to learn without the need to program new skills. However, despite such a long presence of the phenomenon, the term became widely used only some time ago.

It is worth noting that machine learning is a part of a broader department, which is artificial intelligence, which in addition to machine learning also includes evolutionary computing, fuzzy logic, neural networks, robotics and artificial life. This means that machine learning is only part of the high abilities offered by computers.

What exactly is machine learning? First of all, it is based on algorithms. These algorithms process the provided data, learn from it, and then apply the acquired knowledge, e.g. when making decisions. Machine learning is most often based on the so-called Big Data. For this reason, web analytics is extremely important for the machine learning industry, thanks to which this department is able to develop and improve.

What is machine learning for, i.e. how can you use it?

The great support that machine learning offers is that you can segregate and classify a lot of information. For example, it is used in e-mail. Due to specific features, the algorithm assigns particular objects to a given category. This is the case, for example, in the case of spam in the mail – the spam filter decides whether a given message is valuable for the recipient or whether it should go to spam. The program can also learn this through manual administrator moderation, which is supervised learning. They are used in larger websites where spam filters are not a necessary help in controlling all received comments.

Machine learning, however, allows you to group data without prior control – it is the so-called clustering. Data is divided due to similar characteristics, but these do not need to be clustered beforehand for machine learning to occur.

The phenomenon of machine learning is also used when studying relationships between variable data. The program is able to offer analytical key information, including the determination of whether or not the variables are dependent on each other. Such a solution makes it much easier to predict behavior and make long-term conclusions.

The pre-selection of a given data set is also a facilitation offered by machine learning. It allows you to discard random variables that are irrelevant to our project and leave only those from which you can draw further conclusions. The phenomenon is based on the principle of selection – rejection of redundant information and removal of redundant features.

To understand why machine learning is even better, it is worth mentioning a few examples. One of them is the autocorrect of the keyboard. An algorithm running in the program or application uses the most common words and errors to reduce typos you make while typing. It can also highlight potential linguistic errors so that the user pays attention to them. Search engines, including Google, also benefit from the achievements of machine learning. By entering the beginning of the phrase in the search engine window, you immediately get suggestions of similar and most popular search results, which you can click without having to enter the entire word or sentence. This is due to previous experiences and thus you can find the desired information or site faster. Another well illustrative example of the use of machine learning is the recommendation system, e.g. on the Netflix platform. The algorithm remembers the videos that the user watched and, based on their ratings, is able to recommend similar suggestions that may appeal to a specific viewer.

When to implement machine learning in your business?

Remember that using machine learning is a definite step forward compared to traditional manual programming rules. Moreover, the latter become insufficient when too many variables appear in resources, and additionally, when they have many common features and are confusingly similar to each other. In such a situation, it is worth using the help of machine learning.

When considering making changes, bear in mind that the benefits that machine learning should deliver must be tailored exactly to your project. So if you care about high performance, failure-free operation, shortening of working time and increasing efficiency, this solution is just for you!

Can machine learning not work?

When considering the best solution for your company, keep in mind that machine learning will not solve all your problems and that there are situations in which it is not worth using it. Whether to choose it for a given project should result from practical and confirmed premises or purely engineering indications. Only when you are sure that such a solution will bring measurable benefits and will really support your work, you should focus on machine learning.

What is important when making a decision? First of all, you should have high quality data in your dataset. Only then are you able to properly “train” the algorithm. Otherwise, noise may appear, limiting the effectiveness of machine learning. So if you are unsure of the data you have, use the alternative of traditional rules that are much easier to implement.

In a situation where machine learning is an engineering need, and what’s more, it can have a big impact on economic and efficiency factors, consider introducing it. However, remember that it has to be done correctly and adjusted separately from each project. Only then will you be able to use its full potential!

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