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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
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《机器学习实战-ApacheCN.pdf》https://github.com/apachecn/MachineLearning/blob/master/books/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%AE%9E%E6%88%98-ApacheCN.pdf
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https://github.com/77const/MachineLearning#机器学习
第 1 章: 机器学习基础https://github.com/apachecn/MachineLearning/blob/master/docs/1.机器学习基础.md
@毛红动https://github.com/ElmaDavies
第 2 章: KNN 近邻算法https://github.com/apachecn/MachineLearning/blob/master/docs/2.k-近邻算法.md
@尤永江https://github.com/youyj521
第 3 章: 决策树https://github.com/apachecn/MachineLearning/blob/master/docs/3.决策树.md
@景涛https://github.com/jingwangfei
第 4 章: 朴素贝叶斯https://github.com/apachecn/MachineLearning/blob/master/docs/4.朴素贝叶斯.md
@wnma3mzhttps://github.com/wnma3mz
@分析https://github.com/kailian
第 5 章: Logistic回归https://github.com/apachecn/MachineLearning/blob/master/docs/5.Logistic回归.md
@微光同尘https://github.com/DataMonk2017
第 6 章: SVM 支持向量机https://github.com/apachecn/MachineLearning/blob/master/docs/6.支持向量机.md
@王德红https://github.com/VPrincekin
第 7 章: 集成方法（随机森林和 AdaBoost）https://github.com/apachecn/MachineLearning/blob/master/docs/7.集成方法-随机森林和AdaBoost.md
@片刻https://github.com/jiangzhonglian
第 8 章: 回归https://github.com/apachecn/MachineLearning/blob/master/docs/8.回归.md
@微光同尘https://github.com/DataMonk2017
第 9 章: 树回归https://github.com/apachecn/MachineLearning/blob/master/docs/9.树回归.md
@微光同尘https://github.com/DataMonk2017
第 10 章: K-Means 聚类https://github.com/apachecn/MachineLearning/blob/master/docs/10.k-means聚类.md
@徐昭清https://github.com/xuzhaoqing
第 11 章: 利用 Apriori 算法进行关联分析https://github.com/apachecn/MachineLearning/blob/master/docs/11.使用Apriori算法进行关联分析.md
@刘海飞https://github.com/WindZQ
第 12 章: FP-growth 高效发现频繁项集https://github.com/apachecn/MachineLearning/blob/master/docs/12.使用FP-growth算法来高效发现频繁项集.md
@程威https://github.com/mikechengwei
第 13 章: 利用 PCA 来简化数据https://github.com/apachecn/MachineLearning/blob/master/docs/13.利用PCA来简化数据.md
@廖立娟https://github.com/lljuan330
第 14 章: 利用 SVD 来简化数据https://github.com/apachecn/MachineLearning/blob/master/docs/14.利用SVD简化数据.md
@张俊皓https://github.com/marsjhao
第 15 章: 大数据与 MapReducehttps://github.com/apachecn/MachineLearning/blob/master/docs/15.大数据与MapReduce.md
第 16 章: 推荐系统https://github.com/apachecn/MachineLearning/blob/master/docs/16.推荐系统.md
https://github.com/77const/MachineLearning#深度学习deeplearning--正在更新迭代
入门介绍https://github.com/77const/MachineLearning/blob/dev/docs/DeepLearning/1.%E5%85%A5%E9%97%A8%E4%BB%8B%E7%BB%8D.md
https://github.com/77const/MachineLearning#自然语言处理nlp
https://github.com/77const/MachineLearning#第一部分-入门介绍
自然语言处理入门介绍https://github.com/77const/MachineLearning/blob/dev/docs/NLP/1.%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86%E5%85%A5%E9%97%A8%E4%BB%8B%E7%BB%8D.md
https://github.com/77const/MachineLearning#第二部分-机器翻译
机器翻译https://github.com/77const/MachineLearning/blob/dev/docs/NLP/2.%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91.md
https://github.com/77const/MachineLearning#第三部分-篇章分析
篇章分析-内容概述https://github.com/77const/MachineLearning/blob/dev/docs/NLP/3.1.%E7%AF%87%E7%AB%A0%E5%88%86%E6%9E%90-%E5%86%85%E5%AE%B9%E6%A6%82%E8%BF%B0.md
篇章分析-内容标签https://github.com/77const/MachineLearning/blob/dev/docs/NLP/3.2.%E7%AF%87%E7%AB%A0%E5%88%86%E6%9E%90-%E5%86%85%E5%AE%B9%E6%A0%87%E7%AD%BE.md
篇章分析-情感分析https://github.com/77const/MachineLearning/blob/dev/docs/NLP/3.3.%E7%AF%87%E7%AB%A0%E5%88%86%E6%9E%90-%E6%83%85%E6%84%9F%E5%88%86%E6%9E%90.md
篇章分析-自动摘要https://github.com/77const/MachineLearning/blob/dev/docs/NLP/3.4.%E7%AF%87%E7%AB%A0%E5%88%86%E6%9E%90-%E8%87%AA%E5%8A%A8%E6%91%98%E8%A6%81.md
https://github.com/77const/MachineLearning#第四部分-unit-语言理解与交互技术
UNIT-语言理解与交互技术https://github.com/77const/MachineLearning/blob/dev/docs/NLP/4.UNIT-%E8%AF%AD%E8%A8%80%E7%90%86%E8%A7%A3%E4%B8%8E%E4%BA%A4%E4%BA%92%E6%8A%80%E6%9C%AF.md
https://github.com/77const/MachineLearning#未来规划样子
机器学习基础https://github.com/apachecn/MachineLearning/blob/master/docs/1.机器学习基础.md
@片刻https://github.com/jiangzhonglian
KNN 近邻算法https://github.com/apachecn/MachineLearning/blob/master/docs/2.k-近邻算法.md
@微光同尘https://github.com/DataMonk2017
决策树https://github.com/apachecn/MachineLearning/blob/master/docs/3.决策树.md
@景涛https://github.com/jingwangfei
朴素贝叶斯https://github.com/apachecn/MachineLearning/blob/master/docs/4.朴素贝叶斯.md
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@平淡的天https://github.com/friedhelm739
Logistic回归https://github.com/apachecn/MachineLearning/blob/master/docs/5.Logistic回归.md
@景涛https://github.com/jingwangfei
SVM 支持向量机https://github.com/apachecn/MachineLearning/blob/master/docs/6.支持向量机.md
@小王子https://github.com/VPrincekin
@景涛https://github.com/jingwangfei
集成方法https://github.com/apachecn/MachineLearning/blob/master/docs/7.集成方法-随机森林和AdaBoost.md
@片刻https://github.com/jiangzhonglian
随机森林https://github.com/apachecn/MachineLearning/blob/master/docs/7.集成方法-随机森林和AdaBoost.md
@片刻https://github.com/jiangzhonglian
AdaBoosthttps://github.com/apachecn/MachineLearning/blob/master/docs/7.集成方法-随机森林和AdaBoost.md
@片刻https://github.com/jiangzhonglian
回归https://github.com/apachecn/MachineLearning/blob/master/docs/8.回归.md
@微光同尘https://github.com/DataMonk2017
树回归https://github.com/apachecn/MachineLearning/blob/master/docs/9.树回归.md
@微光同尘https://github.com/DataMonk2017
@小王子https://github.com/VPrincekin
K-Means 聚类https://github.com/apachecn/MachineLearning/blob/master/docs/10.k-means聚类.md
@徐昭清https://github.com/xuzhaoqing
利用 Apriori 算法进行关联分析https://github.com/apachecn/MachineLearning/blob/master/docs/11.使用Apriori算法进行关联分析.md
@刘海飞https://github.com/WindZQ
FP-growth 高效发现频繁项集https://github.com/apachecn/MachineLearning/blob/master/docs/12.使用FP-growth算法来高效发现频繁项集.md
@程威https://github.com/mikechengwei
利用 PCA 来简化数据https://github.com/apachecn/MachineLearning/blob/master/docs/13.利用PCA来简化数据.md
@廖立娟https://github.com/lljuan330
利用 SVD 来简化数据https://github.com/apachecn/MachineLearning/blob/master/docs/14.利用SVD简化数据.md
@片刻https://github.com/jiangzhonglian
@Lisanaaahttps://github.com/Lisanaaa
@咸鱼https://github.com/Watermelon233
@咸鱼https://github.com/Watermelon233
NNhttps://github.com/apachecn/MachineLearning/blob/master/docs/DeepLearning/1.入门介绍.md
@红色石头https://github.com/RedstoneWill
@红色石头https://github.com/RedstoneWill
@瑶妹https://github.com/chenyyx
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@片刻https://github.com/jiangzhonglian
Word2Vec 原理介绍https://github.com/apachecn/MachineLearning/blob/master/docs/NLP/Word2Vec.md
@片刻https://github.com/jiangzhonglian
大数据与MapReducehttps://github.com/apachecn/MachineLearning/blob/master/docs/15.大数据与MapReduce.md
推荐系统https://github.com/apachecn/MachineLearning/blob/master/docs/16.推荐系统.md
https://github.com/77const/MachineLearning#项目负责人
@片刻https://github.com/jiangzhonglian
@那伊抹微笑https://github.com/wangyangting
@瑶妹https://github.com/chenyyx
2017-04-08_第一期的总结https://github.com/77const/MachineLearning/blob/dev/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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@那伊抹微笑https://github.com/wangyangting
@瑶妹https://github.com/chenyyx
@Mikehttps://github.com/mikechengwei
https://github.com/77const/MachineLearning#项目贡献者
@侯法超https://github.com/geekidentity
@hello19883https://github.com/hello19883
@徐鑫https://github.com/sheepmen
@ibehttps://github.com/highfei2011
@Arithmetichttps://github.com/LeeMoonCh
@Veyron Chttps://github.com/caopeirui
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@BBruceyuanhttps://github.com/hey-bruce
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https://github.com/77const/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
https://github.com/77const/MachineLearning/blob/dev/images/MainPage/ApacheCN-ML-bilibili-compare.jpg
《机器学习实战-教学版》https://space.bilibili.com/97678687/#!/channel/detail?cid=22486
《机器学习实战-讨论版》https://space.bilibili.com/97678687/#!/channel/detail?cid=13045
机器学习初学者建议 | ApacheCNhttp://www.apachecn.org/map/179.html
MachineLearning(机器学习) 学习路线图http://www.apachecn.org/map/145.html
http://www.acfun.cn/u/12540256.aspx#page=1
https://space.bilibili.com/97678687/#!/channel/index
http://i.youku.com/apachecn
http://study.163.com/course/courseMain.htm?courseId=1004582003
ApacheCN 组织资源http://www.apachecn.org/
https://github.com/77const/MachineLearning#apachecn-组织资源
kaggle: 机器学习竞赛https://github.com/apachecn/kaggle
TensorFlow R1.2 中文文档http://cwiki.apachecn.org/pages/viewpage.action?pageId=10030122
机器学习实战-教学https://github.com/apachecn/MachineLearning
Spark 2.2.0和2.0.2 中文文档http://spark.apachecn.org/
Zeppelin 0.7.2 中文文档http://cwiki.apachecn.org/pages/viewpage.action?pageId=10030467
Pytorch 0.3 中文文档 http://pytorch.apachecn.org/cn/0.3.0/
Sklearn 0.19 中文文档http://sklearn.apachecn.org/
Storm 1.1.0和1.0.1 中文文档http://storm.apachecn.org/
Kibana 5.2 中文文档http://cwiki.apachecn.org/pages/viewpage.action?pageId=8159377
LightGBM 中文文档http://lightgbm.apachecn.org/cn/latest
Kudu 1.4.0 中文文档http://cwiki.apachecn.org/pages/viewpage.action?pageId=10813594
XGBoost 中文文档http://xgboost.apachecn.org/cn/latest
Elasticsearch 5.4 中文文档http://cwiki.apachecn.org/pages/viewpage.action?pageId=4260364
Beam 中文文档http://beam.apachecn.org/
ml.apachecn.org/mliahttp://ml.apachecn.org/mlia
Readme https://github.com/77const/MachineLearning#readme-ov-file
GPL-3.0 license https://github.com/77const/MachineLearning#GPL-3.0-1-ov-file
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