Machine learning has become a term that we often hear about, especially with the advent of Industry 4.0. Machine learning, which is basically a field that explores the work of algorithms that can learn and develop predictions based on data, is also considered a sub-branch of artificial intelligence. While artificial intelligence is a technology that behaves like a human, we can say that machine learning is a field of study aimed at finding algorithms and big data.
Machine learning, which constitutes the agenda of the giant global companies, is used in many different fields, especially in areas such as marketing and customer relationship management. In machine learning, when you feed the general algorithm with data instead of writing codes, the algorithm creates its own logic based on the existing data. Machine learning algorithms analyze data sets such as texts, images, genetic data, numerical measurements etc. and makes various assumptions based on these sets, enabling companies to obtain more accurate results.
In machine learning, the first key point is to create a model. Then comes the data entry process. At this point, it is necessary not to disregard tricks such as selecting data in accordance with the model, converting data when necessary, or even resorting to various data mining methods. The final stage is the comparison of the model with educational data, learning, and the creation of a new model. In this comparison, the results should be assessed by performance analysis taking also into account the classification and prediction goals.
Algorithms in machine learning now decide which images will appear in our timeline while browsing on Facebook, or which products Ebay and Amazon will recommend.
Machine learning is at the heart of popular applications
Today, machine learning is needed in the field of e-commerce and social media platforms as well, whose importance and use are increasing even further. Applications such as Amazon, Facebook, with their millions of users, may face difficulties in understanding and classifying people’s behavior. Machine learning algorithms offer the opportunity to automate the identification of people’s behavior by analyzing the data and creating patterns at this point. Algorithms in machine learning now decide which images will appear in our timeline while browsing on Facebook, or which products Ebay and Amazon will recommend. These algorithms can also make data-driven predictions. The “Did you mean this?” warning given when you mistype a word while searching on Google is also the “product” of the mentioned algorithms.
The underlying fact of this is that previous users have noticed their mistakes after mistyping and immediately searched with correct words afterwards, and Google algorithms saved and stored them in order to correct the entries of subsequent users. In other words, algorithms are actually learning to correct us. Again, the Netflix application, which has gained great popularity recently, also makes use of machine learning and opens up many options for us. Netflix, which offers several preferences to the users based on the preferences of other users who watch the same things, both shows that it “recognizes” you and tries to guarantee that you spend more time in the application.
Machine learning in cyber security
One of the most widely used areas of machine learning is cyber security. Cyber incidents are increasing with the developing technology, as the attackers are developing new ways to penetrate firewalls in order to infiltrate systems. By the time the people responsible for ensuring security find out where the vulnerability originated and prevent possible infiltrations, most of the attackers already damage the system. Until now, it was tried to prevent security vulnerabilities with software developed by programmers. However, when software alone was not enough, more effective systems began to be produced using artificial intelligence and machine learning. Applications developed in the field of cyber-security with machine learning both make the systems safer and can predict where the next attack may come from. These applications, which enable them to be ahead of the attackers by recognizing previously undetected threats, are becoming indispensable for cyber security.
