Manipulate Data Distribution you own this product

prerequisites
intermediate Python • basics of NumPy, pandas, and Jupyter Notebook
skills learned
building and training a deep learning model for text classification • using sklearn module to report model classification metrics • condition based sampling technique for NumPy array
KC Tung
1 week · 4-7 hours per week · ADVANCED
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In this liveProject, you will create an imbalanced dataset from the IMDb movie dataset. Your goal is to make a dataset where positive reviews are the minority. You’ll then test a theory that if you oversample positive reviews, you could rebalance the training data to build and train a text classification model. You finish up by examining the model performance with a confusion table, and basic metrics such as precision, accuracy and recall.
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 interested in common tools for encoding data for NLP. To begin this liveProject, you will need to be familiar with:

TOOLS
  • Intermediate Python, with basics of Numpy and pandas
  • Basics of Jupyter
  • Basics of Google Colab notebook
  • Basics of TensorFlow
TECHNIQUES
  • Basics of NLP
  • Basics of deep learning
  • Handling random shuffles and data selections
  • Ensuring consistent training data length
  • Evaluating model performance with fundamental metrics

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