8.1 Working with Text Data
What we covered in this video lecture
In this lecture, we are covering different ways to work with text data. This includes going from raw to preprocessed text and converting the preprocessed text into feature vectors for machine learning models.
What type of machine learning models can we use? There are classic models for tabular data like logistic regression and multilayer perceptrons. And then, there are sequence models like 1D convolutional networks and recurrent neural networks. Finally, and most importantly, there are large language transformers, which are now state-of-the-art when it comes to working with text.
Additional resources if you want to learn more
If you want to learn more about Tf-idf approach mentioned in this lecture, I made a walkthrough here: https://nbviewer.org/github/rasbt/pattern_classification/blob/master/machine_learning/scikit-learn/tfidf_scikit-learn.ipynb
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