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Title: GitHub - rayboom/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 rayboom/MachineLearning development by creating an account on GitHub.

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

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https://github.com/rayboom/MachineLearning#machinelearning
ApacheCN(apache中文网)http://www.apache.wiki
https://github.com/rayboom/MachineLearning#第一部分--分类
机器学习基础https://github.com/rayboom/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/rayboom/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/rayboom/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/rayboom/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/rayboom/MachineLearning/blob/master/docs/5.Logistic%E5%9B%9E%E5%BD%92.md
支持向量机https://github.com/rayboom/MachineLearning/blob/master/docs/6.%E6%94%AF%E6%8C%81%E5%90%91%E9%87%8F%E6%9C%BA.md
利用AdaBoost元算法提高分类https://github.com/rayboom/MachineLearning/blob/master/docs/7.1.%E5%88%A9%E7%94%A8AdaBoost%E5%85%83%E7%AE%97%E6%B3%95%E6%8F%90%E9%AB%98%E5%88%86%E7%B1%BB.md
随机森林的使用(非课本内容)https://github.com/rayboom/MachineLearning/blob/master/docs/7.2.%E9%9A%8F%E6%9C%BA%E6%A3%AE%E6%9E%97%E7%9A%84%E4%BD%BF%E7%94%A8.md
https://github.com/rayboom/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/rayboom/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/rayboom/MachineLearning/blob/master/docs/9.%E6%A0%91%E5%9B%9E%E5%BD%92.md
https://github.com/rayboom/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/rayboom/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/rayboom/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/rayboom/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/rayboom/MachineLearning#第四部分--其他工具
利用PCA来简化数据https://github.com/rayboom/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/rayboom/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/rayboom/MachineLearning/blob/master/docs/15.%E5%A4%A7%E6%95%B0%E6%8D%AE%E4%B8%8EMapReduce.md
https://github.com/rayboom/MachineLearning#第五部分--项目实战非课本内容
推荐系统https://github.com/rayboom/MachineLearning/blob/master/docs/16.%E6%8E%A8%E8%8D%90%E7%B3%BB%E7%BB%9F.md
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2017-04-08_第一期的总结https://github.com/rayboom/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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