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所在平台: Udemy |
课程主页: https://www.udemy.com/course/python-beginners/
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**Coursera 课程总结：Python 入门与实战项目** 本课程旨在通过项目驱动的方式教授 Python 编程，特别关注机器学习领域的应用。课程从基础的 Python 环境搭建和语法入手，逐步深入到文件处理、常用库（如 NumPy 和 Pandas）的使用，最终实践一个完整的机器学习项目——文章自动打标签。 **核心学习内容：** * **Python 基础:** * 环境配置 (Python, PyCharm IDE) * 变量、数据类型、类型推断与转换 * 函数、代码块（缩进） * 控制流（for 循环, if/else 条件语句） * 数据结构（列表、索引、切片） * 代码注释与 PEP 8 规范 * 文本文件读写 * **机器学习基础概念:** * 什么是机器学习、算法、训练数据 * 特征与标签、向量 * 模型 * **文本处理与机器学习实践:** * 词袋模型 (Bag of Words, BOW)：概念、使用 Scikit-Learn 实现 * 停用词 (StopWords) 的作用与处理 * NumPy 与 Pandas 库：数组、数据处理、与 Python 列表的对比 * TF-IDF：概念、计算方法、使用 Scikit-Learn 实现 * CSR 矩阵与 COO 矩阵的相互转换 * **实战项目：文章自动打标签** * 项目规划与代码实现 * 遍历目录、读取文件 * Vectorizer、Document、Count 的工作原理 * Fit 和 Transform 方法的应用：提取 Vocabulary 和 Count * 处理 Count 和 Vocabulary 数据结构 * 创建 BOW 文本文件 * 限制停用词 **学习方式：** 课程强调“做了才能学到”的理念，通过动手实践一系列代码和完成一个实际的机器学习项目来帮助学习者掌握 Python 及其在机器学习领域的应用。 **适合对象：** 希望通过项目学习 Python，特别是对文本分析和机器学习感兴趣的初学者。
What's the best way to learn any technology , by doing a PROJECT. That's what exactly this tutorial intends to do. This course teaches Python machine learning using project based approach. Below is the full syllabus for the same. Happy Learning.Chapter 1:- Installing Python framework and Pycharm IDE.Chapter 2:- Creating and Running your first Python project.Chapter 3:- Python is case-sensitive Chapter 4:- Variables, data types, inferrence & type()Chapter 5:- Python is a dynamic languageChapter 6:- Comments in pythonChapter 7:- Creating function, whitespaces & indentationChapter 8:- Importance of new lineChapter 9:- List in python, Index, Range & Negative Indexing Chapter 10:- For loops and IF conditionsChapter 11:- PEP, PEP 8, Python enhancement proposal Chapter 12:- ELSE and ELSE IFChapter 13:- Array vs PythonChapter 14:- Reading text files in PythonChapter 15:- Casting and Loss of DataChapter 16:- Referencing external libarariesChapter 17:- Applying linear regression using sklearnChapter 18:- Creatiing classes and objects.Chapter 19:- What is Machine learning?Chapter 20:- Algoritham and Training data.Chapter 21:- Vectors.Chapter 22:- Models in Machine Learning.Chapter 23:- Features and Labels.Chapter 24:- Bag of words.Chapter 25:- Implementing BOW using SKLearn.Chapter 26:- The fit Method.Chapter 27:- StopWords.Chapter 28:- The transform Method.Chapter 29:- Zip and Unzip.Chapter 30:- Project Article Auto tagging.Chapter 31:- Understanding Article auto tagging in more detail.Chapter 32:- Planning the code of the project.Chapter 33:- Looping through the files of the directory.Chapter 34:- Reading the file in the document collectionChapter 35:- Understanding Vectorizer , Document and count working.Chapter 36:- Calling Fit and Transform to extract Vocab and Count.Chapter 37:- Understanding the count and Vocab collection data.Chapter 38:- Count and Vocab structure complexityChapter 39:- Converting CSR matrix to COO matrixChapter 40:- Creating the BOW text file.Chapter 41:- Restricting Stop words.Chapter 42:- Array vs List revisitedChapter 43:- Referencing Numpy and PandasChapter 44:- Creating a numpy arrayChapter 45:- Numpy Array vs Normal Python arrayChapter 46:- Why do we need Pandas ?Chapter 47:- Revising Arrays vs Numpy Array vs PandasChapter 47:- Corupus / Documents, Document and Terms.Chapter 48:- Understanding TFChapter 49:- Understanding IDFChapter 50:- TF IDF.Chapter 51:- Performing calculations of TF IDF.Chapter 52:- Implementing TF IDF using SkLearn Chapter 53:- IDF calculation in SkLearn.
