# man

<https://github.com/datawhalechina/pumpkin-book>

[機器學習：使用Python](http://machine-learning-python.kspax.io/\)%20%20%0A\[%E4%B8%AD%E6%96%87%20Python%20%E7%AC%94%E8%AE%B0]\(https:/github.com/lijin-THU/notes-python\)%20%20%0A\[%E7%94%A8Python%E5%81%9A%E7%A7%91%E5%AD%A6%E8%AE%A1%E7%AE%97]\(http:/old.sebug.net/paper/books/scipydoc/index.html)

[PRML\_Translation.pdf](https://pan.baidu.com/s/1slSwEpJ)

[Andrew NG的新书《Machine Learning Yearning》](https://www.52ml.net/21367.html)

<http://www.52nlp.cn/prml%E8%AF%BB%E4%B9%A6%E4%BC%9A%E5%89%8D%E8%A8%80>

<https://mqshen.gitbooks.io/prml/content/> PRML

<https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet> Scikit-Learn Cheat Sheet: Python Machine Learning

<http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf> Rules of Machine Learning:Best **Practices** for ML Engineering

[140 Machine Learning Formulas.pdf](https://pan.baidu.com/s/1hsQbZHq)

[周志华：机器学习有这两大派别，新人入门别跟错队](http://mp.weixin.qq.com/s?__biz=MzI5NTIxNTg0OA==\&mid=2247485364\&idx=2\&sn=52f2eaab9ff498736751fad30b0a7964)

[机器学习算法集锦：从贝叶斯到深度学习及各自优缺点](https://mp.weixin.qq.com/s?__biz=MzA3MzI4MjgzMw==\&mid=2650723438\&idx=1\&sn=a778051186c0e1fb3cdb4076868fd54a)

[谷歌机器学习应用的四十三条经验法则](http://www.jiqizhixin.com/article/2158)

[Classical equations and diagrams in machine learning](https://github.com/soulmachine/machine-learning-cheat-sheet)

[北京大学王立威教授：机器学习理论的回顾与展望（一）](http://www.leiphone.com/news/201703/jCoh9mtRVr2ipJWA.html)

[北京大学王立威教授：机器学习理论的回顾与展望（二）](http://www.leiphone.com/news/201703/nw3YCxMWbGw8RT1a.html)

[局部可理解的与模型无关的解释（LIME）技术介绍](https://www.oreilly.com.cn/ideas/?p=563)

[32页ppt干货｜谷歌大规模机器学习：模型训练、特征工程和算法选择](http://mp.weixin.qq.com/s/uJhaWEiv_d0IR8qo08_4gA)

[Python code for "Machine learning: a probabilistic perspective"](https://github.com/probml/pyprobml)

[一文读懂量子机器学习：量子算法基石已经奠定](https://mp.weixin.qq.com/s/XWFnRSj_y-W7VHctgQLpdA)

[斯坦福大学2014（吴恩达）机器学习教程中文笔记](https://github.com/fengdu78/Coursera-ML-AndrewNg-Notes)
