The Machine Learning Cheatsheet
Lately, I spent some time on various data science projects: predictive analysis, natural language processing, graph analysis, etc.
Behind the scene, they all share the same machine learning algorithms. Of course, those models barely represent 10 lines of code in my notebooks, thanks to the wonderful open-source libraries accessible today.
But I wanted to go back to the basics and offer a clear picture of how machine learning works, under the hood.
The idea with this project is to create a simple, concise, potentially exhaustive document about the most common machine learning algorithms.
A cheatsheet one could come back to for a quick read, in case of doubt or just to keep things clear.
This cheatsheet focus on how algorithms work: the learning, the predictions, the representation or even the expected inputs. I also added a few business oriented usecases, in order to show the usability of those methods.
But coding is not included in the document, as I consider it highly dependent on the chosen language and library. Furnished documentations often constitute a good, comprehensive knowledge base.
Deep Learning and Reinforcement Learning methods are also not present here, as they surely require their own cheatsheets.
What it looks like
The Machine Learning Cheatsheet is a 5-pages document that can be found on my github.
This cheatsheet is meant to be a constant work in progress, so please feel free to contact me for any possible improvement!