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Title: GitHub - williamsoham/MachineLearning: Machine Learning in Action（机器学习实战） · GitHub

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Description: Machine Learning in Action（机器学习实战）. Contribute to williamsoham/MachineLearning development by creating an account on GitHub.

Open Graph Description: Machine Learning in Action（机器学习实战）. Contribute to williamsoham/MachineLearning development by creating an account on GitHub.

X Description: Machine Learning in Action（机器学习实战）. Contribute to williamsoham/MachineLearning development by creating an account on GitHub.

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【机器学习实战-中文版-带目录版.pdf】https://github.com/apachecn/MachineLearning/tree/master/books
https://github.com/williamsoham/MachineLearning#第一部分--分类
机器学习基础https://github.com/williamsoham/MachineLearning/blob/master/docs/1.%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E7%A1%80.md
k-近邻算法https://github.com/williamsoham/MachineLearning/blob/master/docs/2.k-%E8%BF%91%E9%82%BB%E7%AE%97%E6%B3%95.md
决策树https://github.com/williamsoham/MachineLearning/blob/master/docs/3.%E5%86%B3%E7%AD%96%E6%A0%91.md
基于概率论的分类方法：朴素贝叶斯https://github.com/williamsoham/MachineLearning/blob/master/docs/4.%E6%9C%B4%E7%B4%A0%E8%B4%9D%E5%8F%B6%E6%96%AF.md
Logistic回归https://github.com/williamsoham/MachineLearning/blob/master/docs/5.Logistic%E5%9B%9E%E5%BD%92.md
支持向量机https://github.com/williamsoham/MachineLearning/blob/master/docs/6.%E6%94%AF%E6%8C%81%E5%90%91%E9%87%8F%E6%9C%BA.md
集成方法-随机森林和AdaBoosthttps://github.com/williamsoham/MachineLearning/blob/master/docs/7.%E9%9B%86%E6%88%90%E6%96%B9%E6%B3%95-%E9%9A%8F%E6%9C%BA%E6%A3%AE%E6%9E%97%E5%92%8CAdaBoost.md
https://github.com/williamsoham/MachineLearning#第二部分--利用回归预测数值型数据
预测数值型数据：回归https://github.com/williamsoham/MachineLearning/blob/master/docs/8.%E9%A2%84%E6%B5%8B%E6%95%B0%E5%80%BC%E5%9E%8B%E6%95%B0%E6%8D%AE%EF%BC%9A%E5%9B%9E%E5%BD%92.md
树回归https://github.com/williamsoham/MachineLearning/blob/master/docs/9.%E6%A0%91%E5%9B%9E%E5%BD%92.md
https://github.com/williamsoham/MachineLearning#第三部分--无监督学习
使用K-均值聚类算法对未标注数据分组:k-means聚类https://github.com/williamsoham/MachineLearning/blob/master/docs/10.k-means%E8%81%9A%E7%B1%BB.md
使用Apriori算法进行关联分析https://github.com/williamsoham/MachineLearning/blob/master/docs/11.%E4%BD%BF%E7%94%A8Apriori%E7%AE%97%E6%B3%95%E8%BF%9B%E8%A1%8C%E5%85%B3%E8%81%94%E5%88%86%E6%9E%90.md
使用FP-growth算法来高效发现频繁项集https://github.com/williamsoham/MachineLearning/blob/master/docs/12.%E4%BD%BF%E7%94%A8FP-growth%E7%AE%97%E6%B3%95%E6%9D%A5%E9%AB%98%E6%95%88%E5%8F%91%E7%8E%B0%E9%A2%91%E7%B9%81%E9%A1%B9%E9%9B%86.md
https://github.com/williamsoham/MachineLearning#第四部分--其他工具
利用PCA来简化数据https://github.com/williamsoham/MachineLearning/blob/master/docs/13.%E5%88%A9%E7%94%A8PCA%E6%9D%A5%E7%AE%80%E5%8C%96%E6%95%B0%E6%8D%AE.md
利用SVD简化数据https://github.com/williamsoham/MachineLearning/blob/master/docs/14.%E5%88%A9%E7%94%A8SVD%E7%AE%80%E5%8C%96%E6%95%B0%E6%8D%AE.md
大数据与MapReducehttps://github.com/williamsoham/MachineLearning/blob/master/docs/15.%E5%A4%A7%E6%95%B0%E6%8D%AE%E4%B8%8EMapReduce.md
https://github.com/williamsoham/MachineLearning#第五部分--项目实战非课本内容
推荐系统https://github.com/williamsoham/MachineLearning/blob/master/docs/16.%E6%8E%A8%E8%8D%90%E7%B3%BB%E7%BB%9F.md
https://github.com/williamsoham/MachineLearning#阶段性总结
2017-04-08_第一期的总结https://github.com/williamsoham/MachineLearning/blob/master/report/2017-04-08_%E7%AC%AC%E4%B8%80%E6%9C%9F%E7%9A%84%E6%80%BB%E7%BB%93.md
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https://github.com/williamsoham/MachineLearning#网站视频
知乎问答-爆炸啦-机器学习该怎么入门？https://www.zhihu.com/question/20691338/answer/248678328
MachineLearning(机器学习) 学习路线图http://www.apachecn.org/map/145.html
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https://github.com/williamsoham/MachineLearning#其它中文文档
Sklearn 0.19 中文文档http://sklearn.apachecn.org/
Spark 2.2.0和2.0.2 中文文档http://spark.apachecn.org
Storm 1.1.0和1.0.1 中文文档http://storm.apachecn.org/
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