Object-Oriented Programming (OOP) has become a crucial paradigm for managing the growing complexity of modern software systems, particularly in fields like machine learning, deep learning, large language models (LLM), and data analytics. This work provides a comprehensive introduction to the integration of OOP techniques within these domains, with a focus on improving code modularity, maintainability, and scalability. We begin by outlining the evolution of computing and the rise of OOP, followed by an in-depth discussion of key OOP principles such as encapsulation, inheritance, polymorphism, and abstraction. The practical application of these principles is demonstrated using Python, a widely adopted language in AI and data science. Furthermore, we examine how design patterns and modular programming can be employed to enhance the structure and efficiency of machine learning systems. In subsequent sections, we apply these OOP concepts to real-world AI tasks, including the encapsulation of preprocessing workflows, machine learning model training, and evaluation. Detailed examples illustrate how OOP can be used to build reusable, scalable machine learning systems while maintaining code clarity and reducing redundancy.This work is intended to serve as a bridge for both beginners and experienced developers, equipping them with the necessary knowledge to apply OOP methodologies in AI-driven projects, ultimately fostering the development of more robust and maintainable systems.

翻译：面向对象编程（OOP）已成为管理现代软件系统日益复杂性的关键范式，尤其是在机器学习、深度学习、大语言模型（LLM）和数据分析等领域。本文全面介绍了OOP技术在这些领域中的集成应用，重点关注提升代码模块化、可维护性和可扩展性。我们首先概述计算技术的演进与OOP的兴起，随后深入探讨封装、继承、多态和抽象等核心OOP原则。这些原则的实际应用通过Python（人工智能与数据科学领域广泛采用的语言）进行演示。此外，我们研究了如何运用设计模式与模块化编程来增强机器学习系统的结构与效率。在后续章节中，我们将这些OOP概念应用于实际人工智能任务，包括预处理工作流的封装、机器学习模型训练与评估。通过具体案例详细阐释了如何利用OOP构建可复用、可扩展的机器学习系统，同时保持代码清晰度并减少冗余。本工作旨在为初学者和经验丰富的开发者搭建桥梁，使其掌握在人工智能驱动项目中应用OOP方法论的必备知识，最终促进开发更健壮、更可维护的系统。