In many cases, however, collecting accurate data sets may not be as easy as it sounds. While you may have a lab full of scientists and computers, it will not necessarily produce a data set that you want. Data sets may be hard to obtain because your data may be private, confidential or you simply don't have access to it. If you do not have access to the data, it may cost you money to obtain it legally.
If you don't have access to the data, there is a simpler option. You can use software that can conveniently create data sets for you. A good example of this is Wikipedia. Users upload data to Wikipedia and it automatically creates an accurate, yet well organized data set.
If you want to make your machine learning models better, you can use data sets that are created by other people and use the data sets to train the models. This is called transfer learning. This article will focus only on works that use this technique.
You originally collect the data and convert it into a data set that you use in your prediction model. The data and the machine learning model are both new, and you do not expect this data set to be useful in building a new model to help in the future.
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