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Apply Word Embeddings for Text Classification
Use 1D Convolutions as Feature Extractors for Text in NLP
Perform Binary Text Classification using Deep Learning with Keras
Welcome to this hands-on, guided introduction to Text Classification using 1D Convolutions with Keras. By the end of this project, you will be able to apply word embeddings for text classification, use 1D convolutions as feature extractors in natural language processing (NLP), and perform binary text classification using deep learning. As a case study, we will work on classifying a large number of Wikipedia comments as being either toxic or not (i.e. comments that are rude, disrespectful, or otherwise likely to make someone leave a discussion). This issue is especially important, given the conversations the global community and tech companies are having on content moderation, online harassment, and inclusivity. The data set we will use comes from the Toxic Comment Classification Challenge on Kaggle. To complete this guided project, we recommend that you have prior experience in Python programming, deep learning theory, and have used either Tensorflow or Keras to build deep learning models. We assume you have this foundational knowledge and want to learn how to use convolutions in NLP tasks such as classification. Note: This course works best for learners based in the North America region. We’re currently working on providing the same experience in other regions.
natural-language-processing
embeddings
deep-learning
text-classification
keras
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Introduction and Import Packages
Load and Explore the Data
Data Preparation — Tokenize and Pad Text Data
Prepare Embedding Matrix with Pre-trained GloVe Embeddings
Create the Embedding Layer
Build the Model
Train the Model
Model Evaluation - Classify Toxic Comments
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In a split-screen video, your instructor guides you step-by-step
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Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.
You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.
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