Get started with word2vec

Did you know that more text has been written in the past 5 years than the rest of human history? That’s why natural language processing algorithms like word2vec are so important. Learn the inner workings of word2vec and how to build a word model in Python.

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word2vec can enable you to:

Here are just a few things you'll be able to do with this skill

Identify similar words

word2vec learns the meaning of words in the context of your data, allowing you to compare two words or search for similar words.

Improve NLP models

Many Natural Language Processing model architectures begin with word vectors as the input.

Make Recommendations

The word2vec approach can be applied outside of NLP to learn vector representations of any set of items which occur with “context”, such as songs in play queues or products in a customer’s browsing activity.

What projects will you build?

The full word2vec training algorithm

You’ll gradually build word2vec from the ground up as we explore each piece of the algorithm.

Analogy completion benchmark

You’ll use a word-analogy completion task to evaluate the quality of your word vectors.

Wikipedia toxic comment classification

You’ll build a text classifier which leverages word vectors to predict whether a given Wikipedia comment should be labeled “toxic”.

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