First, thank you for this amazing course, it is very helpful.
I would like to ask about shuffling. In Week 2, it is mentioned that "Shuffling us to rearrange the data so as not to accidentally introduce a sequence bias. Multiple runs will show the data in different arrangements because it gets shuffled randomly. "
Correct me if I am wrong, this does not apply for time series classification right? Because the problem I am working on is detecting certain patterns in EEG signals (brain electrical activity). If we apply suffleing for classification problems, i think this will change the original order of the data, thus not allowing the model to be able to learn the patterns of interest.
Thanks and regards,