In the past, computers followed rules that were explicitly defined step by step by programmers. Every action needed to solve a problem was already determined. As a result, the system could not operate beyond those boundaries. For this reason, systems could not make decisions on their own. They simply executed the instructions given to them.
Today, however, this understanding has fundamentally changed. Systems no longer just follow commands. They extract patterns from data. They accumulate experience over time. As a result, they produce more accurate results.
This change is not only a technical advancement. It also transforms the way we produce knowledge. Computers are no longer just machines that perform calculations. They have become systems that can learn within certain limits.
How Can Machines Learn Without Being Explicitly Programmed?
Machine learning is the answer to this question. In its most general sense, machine learning is a set of computational methods that enable systems to learn from data or experience without being explicitly programmed, and to improve their performance through this learning process. Instead of following fixed rules, the system discovers relationships within data and produces better results over time.
In this process, algorithms form a model by adapting to training data. This model is developed to achieve a specific objective. To do this, the system makes predictions on a given task, evaluates how accurate those predictions are, and updates itself in a way that reduces error. In other words, learning is not based on a single mechanism but on the interaction of multiple components working together.
To better understand this structure, it is necessary to examine the components that make up a machine learning system. These components include
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- the problem or task to be solved,
- the data used to learn the task,
- the model formed through learning from data, and
- the techniques and tools used to develop that model.
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This structure provides a fundamental framework for understanding how a machine learning system is built and how it operates.
The table below summarizes these components:
| Component | Description |
|---|---|
| Task | What problem is the system trying to solve? (e.g., spam detection) |
| Data | What information does the system learn from? (e.g., emails) |
| Model | A structure that stores learned knowledge and makes predictions (e.g., spam classifier) |
| Techniques and Tools | Methods and software used to build and improve the model |
These components work together to form a machine learning system. For example, companies such as Amazon, Facebook, Netflix, Spotify, YouTube, and Walmart use machine learning to predict what you might like based on your past behavior and the actions of users similar to you.
These systems do not simply memorize individual items; they learn general patterns and relationships that shape user preferences. At a deeper level, they consider not only what is selected, but also when, for how long, and in what context interactions occur, using these behavioral signals to refine predictions.
The Difference Between Machine Learning and Artificial Intelligence (AI vs ML)
Artificial intelligence and machine learning are often used interchangeably; however, they do not refer to the same concept. Artificial intelligence is a broad field that aims to develop systems capable of human-like thinking, decision-making, and problem-solving. Machine learning, on the other hand, is an approach within this field that focuses on enabling systems to improve themselves by learning from data.
In other words, artificial intelligence represents the goal to be achieved, while machine learning is one of the methods used to reach that goal. Not every artificial intelligence system uses machine learning; similarly, not every machine learning model produces what we would consider true “intelligence,” but rather generates data-driven predictions. For this reason, understanding machine learning is the first step toward understanding artificial intelligence.
To make this distinction more concrete, consider a contemporary example: an email system that classifies incoming messages as “spam” or “important.” This task is carried out using machine learning: the system learns from past emails and predicts the category of new messages. However, the purpose of the system extends beyond simple classification; in a broader sense, it relates to artificial intelligence goals such as improving user experience, organizing information flow, and supporting decision-making processes. This example clearly shows that machine learning is a tool, while artificial intelligence is a broader objective.
| Criterion | Artificial Intelligence (AI) | Machine Learning (ML) |
|---|---|---|
| Basic Definition | Simulating human intelligence to solve problems | Learning from past data to make decisions |
| Goal | Building systems capable of performing complex tasks | Improving prediction accuracy through learning |
| Approach | Creating systems that can think and decide like humans | Training models that learn from data |
| Scope | A broad field covering all aspects of intelligent systems | A narrower subset focused on learning from data |
| Working Principle | Uses technologies that mimic human decision-making | Produces predictive models through learning algorithms |
| Data Types | Structured, semi-structured, and unstructured data | Typically structured and semi-structured data |
| Methods Used | Logic, decision trees, rule-based systems | Statistical models and learning algorithms |
| Learning Ability | Includes learning, reasoning, and self-correction | Learns from data and improves with new data |
At this point, the essential question becomes: how does a system actually carry out this learning process? In other words, how does a machine learn from data, and what steps does this process involve?
In the next chapter, we will examine the fundamental processes of machine learning step by step.
References
- International Organization for Standardization (ISO). ISO/IEC 23053:2022 Artificial intelligence—Framework for AI systems using machine learning (ML). 2022.
- Tom M. Mitchell, Machine Learning (New York: McGraw-Hill, 1997).
- Stuart J. Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 4th ed. (Pearson, 2021).
- “Machine Learning, Explained.” MIT Sloan School of Management, https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained. Accessed 22 Apr. 2026.
