Scikit-learn is an open-source Python library for all kinds of predictive data analysis. You can perform classification, regression, clustering, dimensionality reduction, model tuning, and data preprocessing tasks. Scikit-learn's unified API interface makes learning how to implement a variety of algorithms and tasks much easier than it would otherwise be. Once you learn the pattern of how to make Scikit-learn calls, you are off and running. The latest KDnuggets exclusive cheatsheet covers the essentials of machine learning with Scikit-learn.

You want to get started with machine learning. You have a foundational understanding of machine learning concepts. The most obvious answer is to get up and running with Scikit-learn. Scikit-learn is an open-source Python library for all kinds of predictive data analysis. You can perform classification, regression, clustering, dimensionality reduction, model tuning, and data preprocessing tasks.

Lately, I spent some time on various data science projects: predictive analysis, natural language processing, graph analysis, etc. 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.

To solve machine learning problems, there is a wide range of different techniques and methods required, some suited better than others. As a data scientist it can be difficult to encapsulate all of them, and choose which work best for specific scenarios. If one is starting out in this space, it suits to understand the different algorithms and core concepts that make up the different aspects of Machine Learning. A recent machine learning glossary created by Brendan Fortuner, titled the "Machine Learning Cheatsheet" provides "brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more." Novices or users new to machine learning can learn many aspects to the foundations and basics of the space via the brief explanations in this guide.

This document is under early stage development. If you find errors, please raise an issue or contribute a better definition!