Machine learning: what it is, basics, and how it works

This is exactly how Netflix’s recommendation algorithms, voice assistants on your phone, email filters, and even medical AI that recognizes tumors in images better than doctors work. Machine learning is everywhere, it just doesn’t shout about itself, it quietly does its job.

In this article, together with Yevhen Kasyanenko, founder of KISS.software, we will break it all down and explain what machine learning is, how it works, and where it really benefits—especially if implemented wisely.

What is machine learning and how does it work?

In simple terms, machine learning is a technology that allows computer systems to learn on their own: from examples, experience, and mistakes. Without strict instructions and constant intervention from programmers. Below, we will discuss the principles of this process in more detail.

Principles of machine learning

We have compiled four approaches that drive progress:

• Training with a teacher. The most straightforward option. You give the system ready-made examples with the correct answers, and it learns from them. Imagine thousands of photos of cats and dogs, labeled by hand. The algorithm memorizes where the ears, tail, and fur are, and then recognizes who is who on its own. This is how spam is filtered, medical diagnoses are made, and predictions are made about when your favorite coffee will run out.
• Training without a teacher. There are no correct answers here—AI looks for patterns on its own. For example, it looks at the behavior of buyers in an online store and notices that some like discounts, while others, for example, like 15-minute delivery. The result is automatic segmentation and personalized recommendations that really work.
• Reinforcement learning. It’s similar to how a child learns: do it right and get a reward; make a mistake and learn a lesson for the future. This is how artificial intelligence is taught for drones, stock trading algorithms, and even game bots that confidently beat champions in strategy games.
• Deep learning. This is already “heavy artillery.” It involves multi-layered neural networks, similar to the human brain. Thanks to this, AI can recognize speech, translate texts, create realistic images, and even compose music or write code.

“Each of these methods is not about how machines learn to understand the world in their own way. The more data you give them, the smarter they become,” emphasizes Yevhen Kasyanenko.

The main stages of an ML model

Now let’s look at how machine learning works in practice:

1. Data collection and preparation. Without good data, no magic will happen. At this stage, everything unnecessary is cleaned up: errors, duplicates, empty values. The cleaner the data, the smarter the model will be.
2. Model training. Next, algorithms come into play, analyzing information and searching for patterns, based on which the models learn to make predictions. This can take anywhere from a couple of minutes to several weeks if there is a lot of data and the task is too complex.
3. Testing. The model is tested on new data to understand whether it has truly “understood” the essence of the task. If something goes wrong, the parameters can be tweaked or the model can be retrained.
4. Real-world application. When the model is ready, it is put into action. It begins to make predictions, look for failures, take on routine tasks, and becomes smarter every day, adapting to new data.

Each ML system has its own path, but the essence is always the same: we take raw data, teach the model to think, and check how it performs. The result is a tool that really works and helps.

Where is machine learning used?

Machine learning is no longer a “technology of the future”; it is with us every day. Here’s where it’s working right now:

• Netflix, YouTube, Spotify, and other services guess what you want to watch or listen to. It’s not magic, it’s ML analyzing your tastes.
• Banks and payments find suspicious transactions and determine who can be trusted with credit.
• Medicine—algorithms trained on a multitude of medical records and more help doctors find diseases in images and make diagnoses with high accuracy.
• Voice assistants—Siri, Alice, and the like understand what you are saying and try to respond appropriately, thanks to machine learning by voice.
• Driverless cars analyze roads, pedestrians, and signs and make decisions on the fly.
• Cities and logistics: AI uses ML to control traffic lights, predict traffic jams, and help deliver goods faster.
• Art – AI agents use machine learning to paint pictures, write music, and even generate scripts.

“ML is already here – it just works in the background and makes your life a little more convenient,” jokes our expert.

Basic algorithms and the essence of machine learning

Machine learning does not work “by magic”; algorithms are at the heart of it all. It’s like a set of tools: each task has its own. Sometimes a simple model will do, and sometimes a powerful neural network is needed. Let’s take a look at the basics of machine learning, what algorithms are most often used and why:

• Linear regression. A classic that takes several parameters and tries to predict the result. For example, the larger the apartment and the closer it is to the center, the more expensive it is. It works in economics, sales, sports, and anywhere else where something needs to be calculated and predicted.
• Decision trees. The “if-then” principle. The algorithm asks itself questions until it reaches an answer. In chatbots, for example, trees help understand what you want and find the right answer.
• Support vector machine (SVM) method. Used for clear division into categories: yes/no, fraudster/not a fraudster, human/not a human. Works especially well in facial recognition and security tasks.
• Neural networks. A powerful thing, inspired by how our brain works. They process a lot of data, “learn” and give results. They are behind voice assistants, auto-translations, and generative neural networks like ChatGPT.
• Gradient boosting. Combines a bunch of simple models into one powerful one. Ideal when you need a super-accurate forecast, such as when assessing credit risk or forecasting demand. Often used in tasks with large amounts of data.

All these algorithms are like tools in a toolbox: some are suitable for quick assessment, others for in-depth analysis. The main thing is to choose the right one for the task.

Current trends in machine learning

Machine learning has come a long way in the last couple of years. Whereas before it simply helped to analyze data, now artificial intelligence can use it to write texts, create images, predict human behavior, and even participate in medical research.

Here’s what’s happening with ML right now:

1. Scalability. Systems like GPT-4 are no longer just “algorithms,” but real giants: billions of parameters, terabytes of data, working only on powerful servers. Because of this, cloud platforms and special chips are actively being developed so that all of this can be handled and neural networks can continue to be trained.
2. ML is moving to smartphones. Previously, everything was in the cloud, but now it’s right on your phone. Assistants, translators, photo recognition, etc. work locally, quickly, and without the internet. Apple and Google devices already have this capability.
3. Algorithms are learning to be “fair.” If the initial data is inaccurate, the conclusions will be the same. Algorithms can repeat biases from the training sample, for example, in personnel selection. Ways to control and correct this are currently being actively sought.
4. Hybrid models. Sometimes one neural network is not enough. Therefore, it is combined with classic ML, for example, when one algorithm works with numbers and another with images.

Machine learning is maturing rapidly and becoming more powerful, smarter, and even closer to us humans. The further we go, the more it will influence how we work, learn, receive medical treatment, communicate, and make everyday decisions in general,” says Yevhen Kasyanenko.

Why is it important to develop ML projects with professionals?

Machine learning is a cool thing, but it does not tolerate carelessness. Everything is important here: how you collect data, how you clean it, which model you choose, and how you verify that it works at all. One wrong setting and instead of useful predictions, you get numbers “out of thin air” or, even worse, decisions that lead to losses.

That’s why ML is definitely not something you should experiment with on a whim. For a model to really help, and not just pretend to be smart, you need experience: knowledge of algorithms, an understanding of data logic, and a clear plan of action. Those who work with such tasks every day have all of this.

How the KISS.software team helps

At KISS.software, led by expert Yevhen Kasyanenko, we are engaged in the complete development of ML solutions for business tasks:

• We select the appropriate algorithm. We don’t guess, we analyze: what is the task, what data, what is the goal, and we select the right model (neural network, gradient boosting, SVM, etc.).
• We prepare and clean the data. We collect, process, and remove noise and everything unnecessary. Because we are confident that how and what you “feed” the model is how it will “think.”
• We test and tune. Models don’t always work the first time. We optimize, test on new data, and achieve high accuracy.
• We integrate into processes and provide support. We will implement the model into your system and will not abandon it halfway through. We will provide support, refinements, and scaling as your business grows.

ML projects are not experiments, but investments, and if you want them to yield results, it is best to work with those who understand them.

Conclusion

Machine learning today is no longer just another digital technology, but a tool with practical benefits. Predicting demand, automating routine tasks, reducing errors, and making decisions faster are all possible if you approach the task wisely. But a quick-fix approach will not work here. For the model to give accurate results, you need expertise: in algorithms, in data, in business tasks.

At KISS.software, we don’t just do AI, we understand the essence of your task and build solutions that really work for your business. Want to launch an ML project — without unnecessary fuss, with a focus on results? Write to us.