TensorFlow for NLP: Text Embedding and Classification

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Coursera Project Network

Learn the fundamentals of Text Embedding

Learn how to build a Text Classification algorithm

Learn how to build NLP models with Tensorflow

Clock2 hours
Comment Dots英语(English)

This guided project course is part of the "Tensorflow for Natural Language Processing" series, and this series presents material that builds on the third course of DeepLearning.AI TensorFlow Developer Professional Certificate, which will help learners reinforce their skills and build more projects with Tensorflow. In this 2-hour long project-based course, you will learn the fundamentals of Text Embedding and Text Classification, and you will learn practically how to use text embeddings for a classification task in the real world and create, train, and test a neural network with Tensorflow using texts, and you will get a bonus deep learning exercise implemented with Tensorflow. By the end of this project, you will have learned text embedding and created a neural network with TensorFlow on text classification. This class is for learners who want to learn how to work with natural language processing and use Python for building neural networks with TensorFlow, and for learners who are currently taking a basic deep learning course or have already finished a deep learning course and are searching for a practical deep learning project with TensorFlow. Also, this project provides learners with further knowledge about creating and training text classification models and improves their skills in Tensorflow which helps them in fulfilling their career goals by adding this project to their portfolios.


Natural Language ProcessingArtificial Neural NetworkDocument ClassificationTensorflowText Embedding



  1. Overview of the project and Import the Libraries

  2. Analyzing the embeddings

  3. Use Embedding in Text Classification

  4. Create and Train the model

  5. Evaluate the model with Predictions






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