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Imagine teaching a young child how to tell the difference between a cat and a dog. The first time they see an animal picture, they may hesitate because they do not yet know the difference. But after seeing several pictures of various animals and hearing comments from people like “this is a cat because it is small and likes to climb” or “that is a dog because it barks and has a long tail,” they gradually build an internal pattern that helps them distinguish. Each time they see a new picture, they add a new detail to their memory, and their next decision becomes faster and more accurate.
This gradual learning is exactly what happens in algorithms, only inside a machine’s mind rather than a human’s. The algorithm does not come preloaded with all knowledge. It starts like a child: without any prior understanding, but it learns from examples and feedback. When you tell the system “this is a dog” and “that is a cat,” it does not see images as we do. Instead it reads them as digital data and begins linking patterns: dogs share a certain size, cats move in certain ways, and so on.
What happens here is the creation of an internal decision rule — one based on experience rather than preprogramming. It is real learning, much like what humans do, but broader in scale and capable of repeating and analyzing. This apparent simplicity is what makes machine learning algorithms so fascinating. They do not need a superhuman intellect; they only require examples, data, and a bit of patience to learn how to perceive and understand the world in their own way.
What Are Algorithms and Why Do We Need Them in Machine Learning
At their core, algorithms are nothing more than organized methods for solving problems. Just as a cake recipe tells you the steps needed to achieve a delicious outcome, algorithms in computing are logical sequences of instructions given to a computer to process data and reach a result.
When we move into machine learning, things become more dynamic and interactive. The algorithm in this context is not merely executed; it is allowed to learn from experience. It is as if we present examples and outcomes and ask it to discover the rules and relationships that connect inputs to outputs on its own. We do not tell it what is correct. Instead, we show it many cases and let it infer those gradually.
Suppose we have an algorithm intended to recognize spam emails. Instead of writing fixed rules such as “if an email contains the word discount, it is spam,” we supply it with hundreds or thousands of messages already classified as spam or not. By analyzing words and patterns, the algorithm learns to differentiate on its own. The more data it sees, the more accurate its classification becomes.
We need these kinds of algorithms because the volume of data in our world exceeds human capacity to process it manually. More importantly, the patterns within this data can be extremely subtle or complex, often eluding human vision. However an algorithm can detect and learn from them, enabling it to make progressively more accurate and intelligent decisions over time.
A New Logic How Do Machine Learning Algorithms Differ from Traditional Programming
In traditional programming, we act as precise leaders guiding the system step by step: if this happens, do that. We set conditions, define steps, and tell the computer every small detail needed to produce the desired result. It is like writing a recipe expecting the same outcome every time when the instructions are precise. But what if the ingredients change? Or the person tasting the dish has different preferences? In traditional programming, the system will break or err because it has not learned how to adapt.
Machine learning algorithms work entirely differently. We do not dictate every instruction. Instead we present example after example and allow the system to observe patterns, test hypotheses, and develop its own strategies. It is as if we give it eyes to see, ears to hear, and ask it to learn how to think based on data.
Since these algorithms learn from experience, they possess something traditional algorithms do not: adaptability. If data or conditions change, there is no need to reprogram from scratch. Instead we retrain them on new cases and they evolve automatically.
Consider recommendation systems used by video streaming services or online stores as an example. In traditional programming, we would write complex rules such as “if the user watched two action movies, suggest a third similar one.” In machine learning we provide the user’s interaction history and it infers preferences on its own based on patterns that might be hard even for humans to notice.
This new logic is what makes machine learning algorithms better suited for a fast-changing, data-rich world that cannot be contained by fixed rules. They do not just execute—they think.
The Relationship between Algorithms and Artificial Intelligence from Heart to Mind
To understand the relationship between algorithms and artificial intelligence, we can imagine AI as a living being striving to think and act like humans. But this being cannot move, respond, or truly “think” on its own without an internal engine. That engine is algorithms.
Artificial intelligence as a vast field aims to build systems able to perform tasks originally exclusive to humans, such as recognizing faces, understanding language, or making complex decisions in dynamic environments. It cannot do this without precise tools that carry out analysis, thinking, and decision processes—these tools are algorithms.
Machine learning algorithms represent the subconscious of this intelligence. They enable it to look at data, form an idea about it, and learn how to behave in similar situations in the future. Just like humans learn from mistakes and past experiences, these algorithms improve with each classification, prediction, or interaction. They are updated automatically to enhance performance.
If we remove algorithms from the AI equation, what remains is an empty structure: a system that executes limited instructions without awareness or flexibility. With algorithms—especially learning ones—the system becomes a dynamic entity that evolves, interacts, and adapts.
In this way, algorithms are not merely computational tools but the backbone that supports artificial intelligence, the heart pumping its learning ability, and the mind giving it the awareness that the world is changing and that it must change as well.
A Prelude to the Coming Series an Enjoyable Journey Inside the Minds of Algorithms
This article is only the first step in a series designed to break down this vast world into understandable and engaging parts. Later, we will delve into key algorithms one by one, such as linear regression, decision tree, and neural networks, explaining them in simple language supported by real-life examples and applications. The goal is not only to understand how these algorithms work but also why we use each one, when, and in what types of problems.
In the end, artificial intelligence is not magic. It is a series of logical steps and equations that have learned to see something deeper than just numbers in data. Algorithms are not as mysterious as they might seem. They are organized logic that can be understood and learned even if you are not a programmer. And the deeper we understand these algorithms, the closer we get to consciously harnessing this technical power that will shape a large part of our future.
The image is illustrative and generated using artificial intelligence