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Given how rapidly the Machine Learning space is evolving, I imagine these will go out of date quickly, but at least as of June 1, 2017, they are pretty current.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!

If you like this post, give it a ❤️ below.

### 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