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. The glossary covers all the essential algorithms undoubtedly needed in any data science profession as well as provides mathematical representations and programming examples.
From a basic walk-through of why linear regression is a foundational machine learning algorithm to an explanation of how the back-propagation phase of neural network functions, the “Machine Learning Cheatsheet” makes for a helpful machine learning review and basics learning guide. If a user needs to brush up on definitions or overviews of specific concepts, the glossary aids in clarifying vital key terms in data science and machine learning. For practice and review, the site provides a well-organized list of resources such as hundreds of data-sets that span many industries/topics, dozens of available machine learning libraries in categorized by programming language as well as numerous ton of research papers.
Consider this “cheatsheet” more of a well laid out library for different aspects of machine learning. This is an on-going project that will elaborate on different aspects of machine learning as time goes on. Stay tuned and keep up-to-date as more parts of the cheatsheet roll out.
