A World Economic Forum study estimates that by 2020, the digital world will increase to 44 zettabytes of data. That number is continually increasing as more people and devices are connected to the Internet. While some of this data is proprietary, much of it is freely available to users themselves or the broader public.
Open-source data has the potential to drastically influence the development of Machine Learning (ML) and Artificial Intelligence (AI). ML and AI both require significant amounts of data to train; data that can be difficult and time consuming to collect. Open-source data can help minimize these difficulties.
In this article, you’ll learn what is open-source data, and some considerations for using OS data to train machine learning algorithms.
What are Open-Source Datasets?
Open-source datasets, also called open data, are data collections that are freely available for access, use, modification, and sharing. This data is often collected and released by governments, academic institutions, or independent agencies.
Open data is made available based on the idea that some data should be freely available. Freely available data helps ensure equal opportunities and fosters democratic existence. The argument is that if data is collected from the public or is collected using government funds, it should be accessible to all.
Benefits ...
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