It is the unprocessed, raw facts that can be extracted from various resources. Data is generated every millisecond and most of the data generated is unstructured. This means it doesn’t have a specific format. This is the reason why many machine learning algorithms don’t give great results even if a large amount of data is fed as input. Data is not in the right format; it is unstructured and hence difficult to process and get consumed.
It is the processed form of data, i.e. data that has been cleaned and made sense. This information gives meaningful insights to users about specific aspects.
Data in machine learning is usually in the form of text that needs to be converted to numbers since it is difficult for machines to infer from text data. Input data to learning algorithms usually has a tabular structure that consists of rows and columns. The columns indicate the name of the feature and the rows have data of every feature.
Data is split into different sets so that a part of the dataset can be trained upon, a part can be validated and a part can be used for testing purposes.
It is important to understand that good quality data (less to no noise, less to no redundancy, less to no discrepancies) in large amounts yields great results when the right learning algorithm is applied on the input data.
In this post, we understood the significance of data in machine learning, and different types of data associated with machine learning.
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Wow what great post about machine learning I never read such a blog before, this is very interesting!
Machine learning will definitely change the future. I like most of you blogs. I love this topic & especially the way you have explained it is really awesome. Thanks for sharing this info. It a nice time to spend on these interesting blogs. Thanks you.