"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), this concept suggests that the technology enables machines to "learn." However, we know that machines are not alive. In reality, what we call "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 efficient programming languages for this purpose. As a review, we will briefly cover some of the broader concepts related to machine learning. The specific explanation of why Python is used as a tool for machine learning will be discussed in the next article. Finally, we’ll provide an easy-to-understand, practical example to give you a hands-on experience.
**Table of Contents:**
- Life is short, I use Python
- First Machine Learning Sample
- Summary of “Introductionâ€
- What is Machine Learning?
As mentioned earlier, "machine learning" has become a popular term due to recent breakthroughs. Its impressive performance in various fields—such as the famous Go master—has sparked interest and admiration across different industries. However, there are also groups that misunderstand machine learning, either viewing it as overly mysterious or too powerful. In reality, everyday phrases like "The weather is really good today," "You just went to eat," or "I studied so much and got something out of it"—these common expressions all reflect the idea of "learning," which is essentially making decisions based on past experiences and new situations. Translating this decision-making process into a computer’s actions is, in its simplest form, what machine learning is about.
Traditionally, computers follow explicit instructions given by humans to produce results. The relationship between input and output is clear, and as long as the instructions are correct, the outcome can be predicted accurately. But in machine learning, this approach is different. The computer still needs human guidance, but the instructions don’t directly lead to the result. Instead, they give the machine the ability to "learn." Based on this, the computer then processes data and learns the final outcome through the provided "learning ability." This result isn't explicitly coded, which leads to a more refined definition: machine learning is a method where computers use data rather than direct instructions to perform tasks. At its core, this relies on statistical principles, particularly the idea of "correlation over causation," which forms the theoretical basis of machine learning. Essentially, it's a process where a computer uses input data and an algorithm to build a model, aiming to predict future unknown data.
Since statistics are central to machine learning, certain mathematical theories are essential. Chapter 4 introduces the PAC framework. Here, we focus on the deeper nature of machine learning under statistical theory: selecting a reasonable hypothesis space and ensuring the model's generalization ability. To clarify:
- **Hypothesis Space**: The range of possible models or functions that the algorithm can choose from.
- **Generalization Ability**: How well the model performs on unseen data.
Note: These concepts align with the PAC Learning framework. Other theoretical frameworks may vary slightly.
From the above discussion, it's clear that machine learning shares similarities with human thinking. This is supported by neuroscience theories behind neural networks and convolutional neural networks in later chapters. However, it's important to understand that machine learning is not about creating "learning robots" or "intelligent beings." Rather, it's a tool humans use to uncover insights from data.
While concerns about "dangerous AI" exist—like Stephen Hawking’s warnings about AI potentially leading to humanity's demise—this book focuses on practical applications. Rest assured, nothing here will cause global destruction. Everyone can read with confidence and enjoyment (σ’ω’σ).
**Common Terminology in Machine Learning**
Machine learning involves many fundamental terms, some of which may seem complex at first. However, they often have simple and intuitive meanings. For example, the hypothesis space and generalization ability were explained earlier. This section introduces and explains these basic terms, focusing on their practical understanding without delving too deeply into mathematics.
Data plays a crucial role in machine learning. Here are key terms:
- **Dataset**: A collection of data. Each individual piece is a **sample**. Unless otherwise stated, this book assumes samples are independent, which is generally reasonable except for special cases like hidden Markov models.
- **Attribute/Feature**: Characteristics of a sample. The value of a feature is called a **feature value**.
- **Feature Space & Sample Space**: The space where features and samples exist.
- **Label Space**: Describes the output space of the model. For classifiers, it's called the **class space**.
Datasets are typically divided into three parts:
- **Training Set**: Used to train the model.
- **Test Set**: Used to evaluate the model’s generalization ability.
- **Cross-Validation Set (CV Set)**: Used to tune model parameters.
Note: Data collection is non-trivial, especially in the "big data" era. UCI is a great resource for real-world datasets.
To illustrate, consider Xiao Ming, who wants to decide whether to wear a mask based on environmental factors. His past year's data becomes the dataset, each day being a sample. Features like visibility, temperature, humidity, and mask-wearing frequency are used to build a model. He might split the data into training, test, and cross-validation sets to refine his model.
Understanding **generalization ability** is vital. It measures how well a model performs on unseen data. Overfitting occurs when a model memorizes the training data, while underfitting happens when it fails to learn patterns. Balancing these is key, and techniques like Structural Risk Minimization (SRM) help achieve this.
Finally, machine learning is important because it helps extract knowledge from data. As manual tasks decrease, the need for systems that can handle ambiguous, conceptual problems increases. Machine learning enables us to make better decisions, from marketing strategies to medical diagnoses. Its applications span deep learning, speech recognition, data mining, and more. In short, machine learning is a powerful and evolving field with immense potential.
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