Imbalanced Text Data

Augment Training Data and Classify Text you own this product

This project is part of the liveProject series Training Models on Imbalanced Text Data
prerequisites
intermediate Python • basics of NumPy, pandas, Jupyter, Google Colab notebooks, TensorFlow, and of NLP
skills learned
merging training and synthetic data • building and training a text classification model • scoring text data with the model
KC Tung
1 week · 4-7 hours per week · ADVANCED
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Look inside
In this liveProject, you’ll augment text-based training data for a sentiment analysis algorithm with artificially generated positive reviews. You’ll merge the synthetic positive reviews with an unbalanced dataset focused on negative reviews, thereby creating a balanced dataset for your model to train on. You’ll train your model, then evaluate its metrics using sklearn.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project author

KC Tung
KC Tung is an AI architect, machine learning engineer, and data scientist who specializes in delivering AI, deep learning, and NLP models across enterprise architectures. As an AI architect at Microsoft, he helps enterprise customers with use-case driven architecture, AI/ML model development/deployment in the cloud, and technology selection and integration best suited for their requirements. He is a Microsoft certified AI engineer and data engineer. He has a PhD in molecular biophysics from the University of Texas Southwestern Medical, and has spoken at the 2018 O'Reilly AI Conference in San Francisco and the 2019 O'Reilly Tensorflow World Conference in San Jose.

prerequisites

This liveProject is for Python programmers looking to augment training data. To begin this liveProject, you will need to be familiar with:

TOOLS
  • Intermediate Python, with basics of NumPy and pandas
  • Basics of Jupyter Notebook
  • Basics of Google Colab notebooks
  • Basics of TensorFlow
TECHNIQUES
  • Basics of NLP
  • Basics of deep learning

you will learn

In this liveProject, you’ll learn how to augment unbalanced datasets with synthetic training data for sentiment analysis.

  • Merging training and synthetic data
  • Building and training a text classification model
  • Scoring text data with the model
  • Evaluating model performance with fundamental classification metrics

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book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.

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  • Augment Training Data and Classify Text project for free

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