Augment Training Data and Classify Text

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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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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.

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

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