There are many facets to Machine Learning. As I started brushing up on the subject, I came across various “cheat sheets” that compactly listed all the key points I needed to know for a given topic. Eventually, I compiled over 20 Machine Learning-related cheat sheets. Some I reference frequently and thought others may benefit from them too. This post contains 27 of the better cheat sheets I’ve found on the web. Let me know if I’m missing any you like.
Given how rapidly the Machine Learning space is evolving, I imagine these will go out of date quickly, but at least as of June 1, 2017, they are pretty current.
If you want all of the cheat sheets without having to download them individually like I did, I created a zip file containing all 27. Enjoy!
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Machine Learning
There are a handful of helpful flowcharts and tables of Machine Learning algorithms. I’ve included only the most comprehensive ones I’ve found.
Neural Network Architectures
Source: http://www.asimovinstitute.org/neural-network-zoo/
Microsoft Azure Algorithm Flowchart
Source: https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-cheat-sheet
SAS Algorithm Flowchart
Source: http://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/
Algorithm Summary
Source: http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/
Source: http://thinkbigdata.in/best-known-machine-learning-algorithms-infographic/
Algorithm Pro/Con
Source: https://blog.dataiku.com/machine-learning-explained-algorithms-are-your-friend
Python
Unsurprisingly, there are a lot of online resources available for Python. For this section, I’ve only included the best cheat sheets I’ve come across.
Algorithms
Source: https://www.analyticsvidhya.com/blog/2015/09/full-cheatsheet-machine-learning-algorithms/
Python Basics
Source: http://datasciencefree.com/python.pdf
Source: https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics#gs.0x1rxEA
Numpy
Source: https://www.dataquest.io/blog/numpy-cheat-sheet/
Source: http://datasciencefree.com/numpy.pdf
Source: https://www.datacamp.com/community/blog/python-numpy-cheat-sheet#gs.Nw3V6CE
Source: https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/numpy/numpy.ipynb
Pandas
Source: http://datasciencefree.com/pandas.pdf
Source: https://www.datacamp.com/community/blog/python-pandas-cheat-sheet#gs.S4P4T=U
Source: https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/pandas/pandas.ipynb
Matplotlib
Source: https://www.datacamp.com/community/blog/python-matplotlib-cheat-sheet
Scikit Learn
Source: https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet#gs.fZ2A1Jk
Source: http://peekaboo-vision.blogspot.de/2013/01/machine-learning-cheat-sheet-for-scikit.html
Source: https://github.com/rcompton/ml_cheat_sheet/blob/master/supervised_learning.ipynb
Tensorflow
Pytorch
Source: https://github.com/bfortuner/pytorch-cheatsheet
Math
If you really want to understand Machine Learning, you need a solid understanding of Statistics (especially Probability), Linear Algebra, and some Calculus. I minored in Math during undergrad, but I definitely needed a refresher. These cheat sheets provide most of what you need to understand the Math behind the most common Machine Learning algorithms.
Probability
Source: http://www.wzchen.com/s/probability_cheatsheet.pdf
Linear Algebra
Source: https://minireference.com/static/tutorials/linear_algebra_in_4_pages.pdf
Statistics
Source: http://web.mit.edu/~csvoss/Public/usabo/stats_handout.pdf
Calculus
Source: http://tutorial.math.lamar.edu/getfile.aspx?file=B,41,N
