For any Python or R practitioner, this article will prove to be a boon. We provide you cheatsheets for the most widely used machine library in Python & R each. Read on to know what’s in store for you.
Python has a rich and healthy ecosystem of various libraries for data analysis. But one of them stands out as the best and most effective library. No points for guessing, it is Scikit-Learn, one of the robust library for machine learning in Python.
Scikit-learn was initially developed by David Cournapeau as a Google summer of code project in 2007. In the same year, Matthieu Brucher joined the project. In 2010 Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort and Vincent Michel of INRIA got involved with the project and made the first public release, February the 1st 2010. Since then, several new contributions have been made to the project.
Scikit-Learn provides a range of supervised & unsupervised algorithms and is built over SciPy. To get a hands-on experience on Scikit-Learn in Python for machine learning, here’s a step by step guide.
The R platform has proved to be one of the most powerful for statistical computing and applied machine learning. CARET (Classification And Regression Training) is one of the biggest projects in R. Caret package is all you to know for solving any supervised machine learning problem.
Caret package is created and maintained by Max Kuhn from Pfizer. Development started in 2005 and was later made open source and uploaded to CRAN. Here’s a practice guide for implementing machine learning with Caret package in R.
Here are cheatsheets for Scikit-Learn and Caret package to help to gain prowess in Python & R respectively. To download the PDFs of these cheatsheets, click here.