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When you start Machine Learning, you will face confusions regarding the correlation of datasets and proper algorithms to use. The answer depends on several factors like available computable resources, size of the data and expected outputs. When facing a similar situation, you will need Machine Learning cheat sheets which will explain the use of functions algorithms and packages.
Cheat sheets on Python
A lot of developers work with R language or python to implement algorithms of Machine Learning. While working on python, you will find python cheat sheet from Data Camp quite useful. It has a quick reference which will guide you through data structures and Machine Learning python packages.
Cheat Sheets on Scikit
Another open source library for python is Scikit-learn which has a wide variety of Machine Learning, cross-validation, visualization of algorithms and preprocessing of data. It’s a highly recommendable cheat sheet for the ones aspiring to become a data scientist.
Flowchart on SAS algorithm
The blog of SAS is an individual read providing plenty of knowledge. The blog has guidelines on using cheat sheets along with considerations that you must think of when choosing an algorithm. The cheat sheet exhibits an easy flowchart which correlates with algorithms and data.
Machine Learning map
There is another cheat sheet on Scikit-learn tutorials which is one of the easiest flowcharts you can find to use and understand. It displays the complete flow with which you can solve a Machine Learning problem. It also has algorithm examples which help you understand its implementation.
This topic is a help-yourself guide which mentions the best three cheat sheets you can use in Machine Learning. It is perfect for anyone who wants to identify and apply Machine Learning algorithms. This domain is evolving at a rapid pace, and so are the algorithms. So, you must understand the algorithms to fit the areas of regression and classification and unsupervised or supervised learning.