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Interactive Word Embeddings using Word2Vec and Plotly

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Overview

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In this 2 hour long project, you will learn how to preprocess a text dataset comprising recipes. You will learn how to use natural language processing techniques to generate word embeddings for these ingredients, using Word2Vec. These word embeddings can be used for recommendations in an online store based on added items in a basket, or to suggest alternative items as replacements when stock is limited. You will build this recommendation/discovery feature in an interactive and aesthetic visualization tool. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Syllabus

  • Interactive Word Embeddings using Word2Vec and Plotly
    • In this 2 hour long project, you will learn how to preprocess a text dataset comprising recipes, and prepare the data for use in a word embedding model. You will learn how Word2Vec works, and how to implement this model using Gensim. You will learn about visualizing the results using a similarity matrix, and then build a network graph using NetworkX on top of this. You will learn how to build an visual tool to explore this data in a manner that is both interactive and aesthetically unmatched, using Plotly. This tool can then be used for interactive recommendations, or similar item discovery, for example, to be used in an online supermarket store recommending additional items to be purchased, or offering effective alternatives when there is no stock of a desired item.

Taught by

Ari Anastassiou

Reviews

4.5 rating at Coursera based on 42 ratings

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