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Vectorize text using TF-IDF

with Derek Jedamski

Build upon your foundational knowledge of natural language processing (NLP) by exploring more complex topics such as word2vec, doc2vec, and recurrent neural networks.

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Leveraging the power of messy text data

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What you should know

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What tools you need

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Using the exercise files

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What is NLP?

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NLTK setup

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Reading text data into Python

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Cleaning text data

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Vectorize text using TF-IDF

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Building a model on top of vectorized text

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What is word2vec?

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What makes word2vec powerful?

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How to implement word2vec

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How to prep word vectors for modeling

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What is doc2vec?

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What makes doc2vec powerful?

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How to implement doc2vec

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How to prep document vectors for modeling

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What is a neural network?

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What is a recurrent neural network?

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What makes RNNs so powerful for NLP problems?

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Preparing data for an RNN

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How to implement a basic RNN

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Prep the data for modeling

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Build a model on TF-IDF vectors

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Build a model on word2vec embeddings

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Build a model on doc2vec embeddings

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Build an RNN model

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Compare all methods using key performance metrics

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Key takeaways for advanced NLP modeling techniques

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How to continue advancing your skills