Every machine learning (ML) model demands data to train it. If your model isn’t predicting Titanic survival or iris species, then acquiring a dataset might be one of the most time-consuming parts of your model-building process—second only to data cleaning.
What data cleaning looks like varies from dataset to dataset. For example, the following is a set of images tagged robin that you might want to use to train an image recognition model on bird species.
That nest might count as dirty data, and some model applications may make it inappropriate to include American and European robins in …
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