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所在平台: Udemy |
课程主页: https://www.udemy.com/course/from-0-to-1-python/
课程评论：没有评论
课程名称：从0到1：轻松学习Python编程 课程概述：本课程使用Python 2.7进行代码演示，并提供了丰富的注释源代码资源，包含Python 2和Python 3的示例。课程覆盖了以下内容： 1. **Python基础知识**：学习功能性语言构造，Python语法，列表、字典，函数及函数对象，Lambda函数，迭代器，异常处理以及文件操作。 2. **数据库操作**：掌握用于数据操作的必要数据库知识，以便在Python中进行数据处理。 3. **自动生成电子表格**：通过使用xlsxwriter消除报告任务的繁琐，自动生成结合数据库操作的报告和电子表格。 4. **文本处理与自然语言处理**：利用Python强大的文本处理工具，例如nltk等。 5. **网页爬虫**：使用Beautiful Soup进行网页爬取，例如爬取《纽约时报》和《华盛顿邮报》的文章。 6. **机器学习**：应用sk-learn技术来实施机器学习方法，如KMeans聚类。 **实操演练**： - 演练1：从印度国家证券交易所下载一个压缩文件，解压并处理以找到当天交易最活跃的三种证券。 - 演练2：将三年的股票交易时间序列数据存储到数据库中，并按需生成特定股票代码的时间序列报告。 - 演练3：抓取新闻文章URL并自动摘要为三句话。 - 演练4：抓取报纸和博客，应用多种机器学习技术进行分类和聚类分析。 本课程为希望从基础开始学习Python编程的学员提供了全面的实践和理论支持。
A Note on the Python versions 2 and 3: The code-alongs in this class all use Python 2.7. Source code (with copious amounts of comments) is attached as a resource with all the code-alongs. The source code has been provided for both Python 2 and Python 3 wherever possible. What's Covered: Introductory Python: Functional language constructs; Python syntax; Lists, dictionaries, functions and function objects; Lambda functions; iterators, exceptions and file-handlingDatabase operations: Just as much database knowledge as you need to do data manipulation in PythonAuto-generating spreadsheets: Kill the drudgery of reporting tasks with xlsxwriter; automated reports that combine database operations with spreadsheet auto-generationText processing and NLP: Python's powerful tools for text processing - nltk and others. Website scraping using Beautiful Soup: Scrapers for the New York Times and Washington PostMachine Learning: Use sk-learn to apply machine learning techniques like KMeans clustering Hundreds of lines of code with hundreds of lines of comments Drill #1: Download a zip file from the National Stock Exchange of India; unzip and process to find the 3 most actively traded securities for the dayDrill #2: Store stock-exchange time-series data for 3 years in a database. On-demand, generate a report with a time-series for a given stock tickerDrill #3: Scrape a news article URL and auto-summarize into 3 sentencesDrill #4: Scrape newspapers and a blog and apply several machine learning techniques - classification and clustering to these
