MachineLearning-PythonPractice

机器学习-Python实践书籍对应的Jupyter代码

看过很多课程和书籍，有很多内容是直接拿出来最优结果与调参后的方法，使得初期学习不那么容易理解
本书还是基础介绍里面比较全面和系统的，比较建议初期去看，大概两天以内就可以理解和消化完

Name		Name	Last commit message	Last commit date
Latest commit History 12 Commits
img		img
001-初识机器学习.ipynb		001-初识机器学习.ipynb
002-安装一些必要模块.ipynb		002-安装一些必要模块.ipynb
003-第一个机器学习项目.ipynb		003-第一个机器学习项目.ipynb
004-Python和SciPy速成.ipynb		004-Python和SciPy速成.ipynb
005-数据导入.ipynb		005-数据导入.ipynb
006-数据理解.ipynb		006-数据理解.ipynb
007-数据可视化.ipynb		007-数据可视化.ipynb
008-数据预处理.ipynb		008-数据预处理.ipynb
009-数据特征选定.ipynb		009-数据特征选定.ipynb
010-评估算法.ipynb		010-评估算法.ipynb
011-算法评估矩阵.ipynb		011-算法评估矩阵.ipynb
012-审查分类算法.ipynb		012-审查分类算法.ipynb
013-审查回归算法.ipynb		013-审查回归算法.ipynb
014-算法比较.ipynb		014-算法比较.ipynb
015-自动流程.ipynb		015-自动流程.ipynb
016-集成算法.ipynb		016-集成算法.ipynb
017-算法调参.ipynb		017-算法调参.ipynb
018-持久化加载模型.ipynb		018-持久化加载模型.ipynb
019-预测模型项目模板.ipynb		019-预测模型项目模板.ipynb
020-回归项目实例.ipynb		020-回归项目实例.ipynb
021-二分类实例.ipynb		021-二分类实例.ipynb
022-文本分类实例.ipynb		022-文本分类实例.ipynb
22-data.rar		22-data.rar
README.md		README.md
finalized_model.sav		finalized_model.sav
finalized_model_joblib.sav		finalized_model_joblib.sav
housing.csv		housing.csv
iris.csv		iris.csv
pima_data.csv		pima_data.csv
sonar.all-data.csv		sonar.all-data.csv
test_with.txt		test_with.txt

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