by Shubhi Asthana

**You need these cheat sheets if you’re tackling Machine Learning Algorithms.**

When I started learning Machine Learning (ML) two years back, I had many questions around which algorithms to use, how to correlate it to datasets, etc. The answer depended on many factors like the size of data, expected output, and available computational resources. I was then introduced to the ML cheat sheets which acquainted me with the frequently used algorithms, packages, and functions.

This post contains the top three cheat sheets that I would recommend to a beginner who is interested in identifying and applying ML algorithms to different problems. Given how rapidly this domain is evolving, the trending algorithms are progressing too. Therefore, it is important to understand the algorithms that help fit the areas of supervised and unsupervised learning, classification and regression, and so on.

**SAS Algorithm Flowchart**

Source: https://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/

The SAS blog itself is a great read. The link shows how to use the cheat sheet as well as considerations when choosing an algorithm. The cheat sheet shows an easy to use flowchart correlating data to the algorithms.

**Python and Scikit Cheat sheets**

Most developers work in Python or R language for implementing the ML algorithms. I work in Python, and so the following two cheat sheets have been very useful to me.

Source: https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf

The Python cheat sheet was prepared by DataCamp, and can be used as a quick reference to guide through ML Python packages and data structures as well.

Source: https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf

Scikit-learn is an open-source Python library that implements a wide variety of ML, preprocessing of data, and cross validation as well as visualization of algorithms. This library belongs to the must know for every aspiring data scientist, so I highly recommend this cheat sheet.

**User friendly Machine Learning Map**

Source: http://scikit-learn.org/stable/tutorial/machine_learning_map/

This cheat sheet is available on the scikit-learn tutorials and is one of the easiest flowcharts to understand and use. At the link above, you have the complete flow for solving a ML problem, and you can also click on any algorithm on the map to understand its implementation.

Share & Learn! Do add your favorite cheat sheet in the comments below.