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Build Your First Text Classifier in Python with Logistic Regression
Text classification is the automatic process of predicting one or more categories given a piece of text. For example, predicting if an email is legit or spammy. Thanks to Gmail’s spam classifier, I don’t see or hear from spammy emails! Other than spam detection, text classifiers can be used to determine sentiment in social media … Build Your First Text
10+ Examples for Using CountVectorizer
Learn how to maximize the use of CountVectorizer such that you are not just computing counts of words, but also preprocessing your text data appropriately as well as extracting additional features from your text dataset.
Easily Access Pre-trained Word Embeddings with Gensim
What are pre-trained embeddings and why? Pre-trained word embeddings are vector representation of words trained on a large dataset. With pre-trained embeddings, you will essentially be using the weights and vocabulary from the end result of the training process done by….someone else! (It could also be you) One benefit of using pre-trained embeddings is that … Easily Access Pre-trained Word
How to Use Tfidftransformer & Tfidfvectorizer?
Scikit-learn’s Tfidftransformer and Tfidfvectorizer aim to do the same thing, which is to convert a collection of raw documents to a matrix of TF-IDF features. The differences between the two modules can be quite confusing and it’s hard to know when to use which. This article shows you how to correctly use each module, the … How to Use Tfidftransformer