原作者Robbie Allen
编译 CDA 编译团队
本文为 CDA 数据分析师原创作品,转载需授权
机器学习涉及到方方面面。当我开始学习这一领域时,遇到了各种“速查表”,这些速查表中列出了我所需要知道的所有要点。最终,我整合了共27张机器学习相关的速查表。我经常参考这些速查表,希望其他人也能够从中受益。
以下是一些实用的流程图和机器学习算法表。
来源:
http : //www.asimovinstitute.org/neural-network-zoo/
The Neural Network Zoo
来源:
https : //docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-cheat-sheet
Microsoft Azure机器学习工作室的机器学习算法速查表
来源:
http : //blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/
SAS:我应该使用哪种机器学习算法?
来源:
http : //machinelearningmastery.com/a-tour-of-machine-learning-algorithms/
机器学习算法之旅
来源:
http : //thinkbigdata.in/best-known-machine-learning-algorithms-infographic/
哪些是最知名的机器学习算法?
来源:
https://blog.dataiku.com/machine-learning-explained-algorithms-are-your-friend
Python有很多在线资源,该部分我仅列出我发现的最优质的速查表。
来源:
https : //www.analyticsvidhya.com/blog/2015/09/full-cheatsheet-machine-learning-algorithms/
来源:
http : //datasciencefree.com/python.pdf
来源:
https : //www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics#gs.0x1rxEA
来源:
https : //www.dataquest.io/blog/numpy-cheat-sheet/
来源:
http : //datasciencefree.com/numpy.pdf
来源:
https : //www.datacamp.com/community/blog/python-numpy-cheat-sheet#gs.Nw3V6CE
来源:
https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/numpy/numpy.ipynb
来源:
http : //datasciencefree.com/pandas.pdf
来源:
https://www.datacamp.com/community/blog/python-pandas-cheat-sheet#gs.S4P4T=U
来源:
https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/pandas/pandas.ipynb
来源:
https://www.datacamp.com/community/blog/python-matplotlib-cheat-sheet
来源:
https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/matplotlib/matplotlib.ipynb
来源:
https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet#gs.fZ2A1Jk
来源:
http : //peekaboo-vision.blogspot.de/2013/01/machine-learning-cheat-sheet-for-scikit.html
来源:https://github.com/rcompton/ml_cheat_sheet/blob/master/supervised_learning.ipynb
来源:
https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/basic_operations.ipynb
来源:
https://github.com/bfortuner/pytorch-cheatsheet
如果你真的想理解机器学习,那么你需要对统计学(特别是概率),线性代数和一定微积分知识有较好的把握。以下的速查表包括:你需要理解的主要机器学习算法相关的数学概念。
来源:
http : //www.wzchen.com/s/probability_cheatsheet.pdf
来源:
https : //minireference.com/static/tutorials/linear_algebra_in_4_pages.pdf
4页讲解线性代数
来源:
http : //web.mit.edu/~csvoss/Public/usabo/stats_handout.pdf
来源:
http : //tutorial.math.lamar.edu/getfile.aspx?file=B,41,N
原文链接:
https://unsupervisedmethods.com/cheat-sheet-of-machine-learning-and-python-and-math-cheat-sheets-a4afe4e791b6
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