A comprehensive learning path: from Python basics to AI, NLP, and quantitative trading system development.

Welcome to my Python learning repository! As an app developer, I created this project to document my complete learning process with Python, from basic syntax to advanced applications, particularly in artificial intelligence, natural language processing, and quantitative trading system development.

• Master Python core syntax and programming paradigms
• Understand Python's application in data analysis
• Explore Python's practical use in AI
• Dive deep into natural language processing technologies
• Develop a basic quantitative trading system
• Integrate Python skills with mobile application development

• Python installation and environment setup
• Basic data types and variables
• Control flow structures (conditionals, loops)
• Function definition and usage
• Modules and packages
• File operations
• Exception handling
• Practical project: Simple command-line tool development

• Object-oriented programming
• Advanced data structures
• Regular expressions
• Decorators and generators
• Concurrent programming
• Network programming basics
• Practical project: Web scraper development

• NumPy basics
• Pandas for data processing
• Matplotlib/Seaborn for data visualization
• Data cleaning and preprocessing
• Basic statistical analysis
• Practical project: Financial data analysis report

• Machine learning fundamental concepts
• Scikit-learn in practice
• Introduction to deep learning
• TensorFlow/PyTorch basics
• Introduction to computer vision
• Practical project: Building a simple prediction model

• NLP basic concepts
• Text preprocessing techniques
• Word vectors and embeddings
• Using NLTK and SpaCy libraries
• Sentiment analysis and text classification
• Sequence models (RNN, LSTM, GRU)
• Transformer model architecture
• BERT and GPT model applications
• Practical project: Building a chatbot or text analysis tool

• Financial market fundamentals
• Trading strategy design
• Backtesting system construction
• Risk management
• Real-time trading interface integration
• NLP applications in financial market analysis
• Practical project: Developing a complete quantitative trading system

• AI applications in quantitative trading
• Web interface development (Flask/Django)
• Mobile app and Python backend integration
• System optimization and deployment
• Multimodal model exploration
• Practical project: Building a trading strategy analysis platform

/
├── basics/              # Python fundamentals learning code
├── advanced/            # Advanced Python concepts practice
├── data_analysis/       # Data analysis related code and notes
├── ai_learning/         # AI learning practice
├── nlp/                 # Natural language processing learning and projects
│   ├── basics/          # NLP basic concepts
│   ├── text_processing/ # Text preprocessing
│   ├── embeddings/      # Word embedding practice
│   ├── models/          # Various NLP model implementations
│   └── projects/        # NLP project practice
├── quant_trading/       # Quantitative trading system development
├── projects/            # Comprehensive project practice
└── resources/           # Learning resources and references

• Official documentation: Python Documentation
• Data analysis: Pandas Documentation
• Machine learning: Scikit-learn
• Deep learning: TensorFlow / PyTorch
• Natural language processing:
  • NLTK
  • SpaCy
  • Hugging Face Transformers
• Quantitative trading: Quantlib

I plan to update learning notes and code examples 2-3 times per week, documenting insights and challenges throughout the learning process.

If you're also interested in Python, AI, NLP, or quantitative trading, welcome to exchange ideas! You can participate in this learning project through Issues or Pull Requests.

欢迎来到我的Python学习仓库！作为一名APP开发工程师，我创建了这个项目来记录我学习Python的完整过程，从基础语法到高级应用，特别是在人工智能、自然语言处理和量化交易系统开发方面的探索。

• 掌握Python核心语法和编程范式
• 理解Python在数据分析中的应用
• 探索Python在AI领域的实践
• 深入学习自然语言处理技术
• 开发一个基础的量化交易系统
• 将Python技能与移动应用开发结合

• Python安装与环境搭建
• 基本数据类型与变量
• 控制流结构（条件语句、循环）
• 函数定义与使用
• 模块与包
• 文件操作
• 异常处理
• 项目实践：简单命令行工具开发

• 面向对象编程
• 高级数据结构
• 正则表达式
• 装饰器与生成器
• 并发编程
• 网络编程基础
• 项目实践：Web爬虫开发

• NumPy基础
• Pandas数据处理
• Matplotlib/Seaborn数据可视化
• 数据清洗与预处理
• 统计分析基础
• 项目实践：金融数据分析报告

• 机器学习基础概念
• Scikit-learn实践
• 深度学习入门
• TensorFlow/PyTorch基础
• 计算机视觉入门
• 项目实践：简单预测模型构建

• NLP基础概念
• 文本预处理技术
• 词向量和词嵌入
• NLTK和SpaCy库使用
• 情感分析与文本分类
• 序列模型（RNN, LSTM, GRU）
• Transformer模型架构
• BERT和GPT模型应用
• 项目实践：构建聊天机器人或文本分析工具

• 金融市场基础知识
• 交易策略设计
• 回测系统构建
• 风险管理
• 实时交易接口对接
• NLP在金融市场分析中的应用
• 项目实践：开发一个完整的量化交易系统

• AI在量化交易中的应用
• Web接口开发（Flask/Django）
• 移动应用与Python后端集成
• 系统优化与部署
• 多模态模型探索
• 项目实践：构建交易策略分析平台

/
├── basics/              # Python基础学习代码
├── advanced/            # Python进阶概念实践
├── data_analysis/       # 数据分析相关代码和笔记
├── ai_learning/         # AI学习实践
├── nlp/                 # 自然语言处理学习和项目
│   ├── basics/          # NLP基础概念
│   ├── text_processing/ # 文本预处理
│   ├── embeddings/      # 词嵌入实践
│   ├── models/          # 各类NLP模型实现
│   └── projects/        # NLP项目实践
├── quant_trading/       # 量化交易系统开发
├── projects/            # 综合项目实践
└── resources/           # 学习资源和参考资料

• 官方文档：Python Documentation
• 数据分析：Pandas Documentation
• 机器学习：Scikit-learn
• 深度学习：TensorFlow / PyTorch
• 自然语言处理：
  • NLTK
  • SpaCy
  • Hugging Face Transformers
• 量化交易：Quantlib

我计划每周更新2-3次学习笔记和代码示例，记录学习过程中的心得和挑战。

如果你也对Python、AI、NLP或量化交易感兴趣，欢迎交流讨论！可以通过Issues或Pull Requests参与到这个学习项目中来。

This learning roadmap will be continuously adjusted and improved based on actual learning progress and new learning directions.