"Machine learning" has become a buzzword in recent years, even if not everyone is familiar with it. Literally translated from the English term "Machine Learning" (ML for short), it suggests that this technology enables machines to "learn." However, we all know that machines are not alive; the so-called "learning" is ultimately just a series of operations programmed by humans. This process can be implemented in many ways, and Python is one of the most user-friendly and powerful programming languages for this purpose. As a review, we will briefly cover some key concepts related to machine learning. The specific explanation of why Python is used in this field will be discussed in the next article. In the end, we will provide a practical example that is easy to understand, giving you an intuitive experience.
Table of Contents:
Life is short, I use Python
First Machine Learning Sample
Summary of "Introduction"
What is Machine Learning?
As mentioned earlier, "machine learning" has gained popularity due to recent breakthroughs. Its impressive performance in various fields—such as the Go master being one of the most notable examples—has sparked interest and awe among people from different backgrounds. At the same time, there are growing misunderstandings about it. Some may think it's too mysterious or too versatile. However, in reality, even simple everyday expressions like “The weather is really good today,†“You just went to eat,†or “I studied so hard and finally got something out of it†contain the idea of “learning.†These are decisions made based on past experiences and new situations. Translating this decision-making process into a computer’s task is essentially the simplest definition of "machine learning."
Traditionally, computers follow a set of explicit instructions provided by humans to produce a result. The cause-and-effect relationship in such processes is clear, and if the instructions are well-defined, the results can be accurately predicted. However, in machine learning, this traditional approach is broken. While the computer still needs human-provided instructions, these are not direct commands but rather a set of rules that give the machine the ability to "learn." Based on this, the computer then receives data and uses its "learning ability" to derive the final result. This result is not directly coded but is instead obtained through an algorithm. Thus, a more refined definition of machine learning is that it is a way for computers to perform tasks using data rather than explicit instructions. At the core of this is the concept of statistics, particularly the idea of "correlation over causation," which forms the theoretical foundation of machine learning.
In statistical learning theory, the goal is to find a hypothesis space—a set of possible models—and select the one with the best generalization ability. Generalization refers to how well a model performs on unseen data. This is crucial because training data is only a small part of the overall data space. If a model focuses too much on training data, it might overfit, while ignoring it could lead to underfitting. Finding the right balance between these two extremes is essential, and techniques like Structural Risk Minimization (SRM) help achieve this by minimizing the upper bound of risk, not just the empirical risk.
Cross-validation is a technique used to evaluate a model's performance and prevent overfitting. Common methods include k-fold cross-validation, where the data is split into k parts, and each part is used as a test set once. Leave-one-out is a special case where k equals the number of samples. Another common method is the train-test split, where 70% of the data is used for training and 30% for testing.
Machine learning is important because it helps us extract meaningful insights from vast amounts of data. It powers applications like image recognition, speech processing, data mining, and natural language understanding. From self-driving cars to recommendation systems, machine learning is transforming industries. While it cannot guarantee perpetual popularity, its impact is undeniable, and its potential continues to grow.
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