Data Scientist Resource
机器学习背影，历史，流派介绍
牛B教程，贴心指引，还free:
学习方法，资源
http://www.open-open.com/lib/view/open1456751835546.html利用Python，四步掌握机器学习
https://github.com/dataquestio/data-science-blogs
http://lib.csdn.net/BaiHuaXiu123/216033/chart/人工智能之机器学习图谱中级机器学习图谱
*如何快速成为数据分析师https://www.zhihu.com/question/29265587
数据科学家自我修养 a way to data scientisthttp://mooc.guokr.com/post/604911/
http://datasciencemasters.org/
开源数据科学
DataSet
Algrithm ,算法比较，算法选择
种类算法分析对比：https://static.coggle.it/diagram/WHeBqDIrJRk-kDDY
算法选择KKhttps://www.52ml.net/15063.html
ML - andrew NG 课程的总结笔记:
http://blog.csdn.net/u012717411/article/details/50531678http://blog.csdn.net/column/details/machine-learning1.htmlhttps://www.yalewoo.com/andrew_ng_machine_learning_notes_11_photo_ocr.htmlhttp://www.holehouse.org/mlclass/
https://github.com/jdwittenauer/ipython-notebookspython 实现习题https://github.com/kaleko/CourseraML. Python implement exercisehttps://github.com/JWarmenhoven/Coursera-Machine-Learninghttp://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/
Sklearn
https://muxuezi.github.io/categories/machine-learning.html中文
<商务经济统计学> Web analysis 2.0
练习:http://www.scipy-lectures.org/https://jmetzen.github.io/2015-01-29/ml_advice.html
程序猿的数据科学与机器学习实战手册:https://github.com/wxyyxc1992/DataScience-And-MachineLearning-Handbook-For-Coders
Numpy + Pandas
https://github.com/jasonding1354/pyDataScienceToolkits_Base
Pandas :
http://pandas.pydata.org/pandas-docs/stable/index.html
https://github.com/David9998/pydata-book. Python for data analysis source code
scipy :
https://docs.scipy.org/doc/scipy/reference/index.html
大数据集 处理
Sklearn 增量学习 -> 针对 大数据量
PySpark + Scikit-learn = Sparkit-learn
https://github.com/lensacom/sparkit-learn
很好的ML练习题：
http://blog.jobbole.com/85680/
https://github.com/wxyyxc1992/DataScience-And-MachineLearning-Handbook-For-Codershttps://zhuanlan.zhihu.com/p/23339926https://github.com/hangtwenty/dive-into-machine-learning****
https://www.datacamp.com/community/tutorials/scikit-learn-tutorial-baseball-1#gs.mmtG4yw
datacamp ,在网页上直接submit code 练习 Exploratory data analysis (EDA) (Statistical Thinking in Python (Part 1)) =>
https://www.datacamp.com/community/tutorials/exploratory-data-analysis-python
可视化:
https://www.douban.com/group/topic/44748285/史上很强的 coursera 分享
https://turi.com/learn/translator/pandas vs R vs GraphLab
在github 找资料, 学新东西去官网
https://github.com/detailyang/awesome-cheatsheet. 看了之后你会觉得,要 充分利用前人的智慧,不要重复造轮子.
一些coursera 数据科学课程清单:
http://blog.csdn.net/datacastle/article/details/52228028
blog; 总结了很多
http://blog.csdn.net/baihuaxiu123?viewmode=listhttp://blog.csdn.net/baihuaxiu123/article/details/69488610
http://www.52caml.com/home/index.htmlML深入浅出 - 总结得很好