Manipulate Data Distribution

This project is part of the Training Models on Imbalanced Text Data bundle.
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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liveProject This project is part of the Training Models on Imbalanced Text Data bundle. liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $19.99 $29.99 you save: $10 (33%)
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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.

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 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

you will learn

In this liveproject, you’ll learn sampling and shuffling techniques to handle data imbalance.

  • Condition based sampling technique for NumPy array
  • Selecting data using randomized index
  • Replicating NumPy arrays
  • Padding arrays to same length
  • Building and training a deep learning model for text classification
  • Using sklearn module to report model classification metrics
  • Handling random shuffles and data selections

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
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While within the liveProject platform, get help from other participants and our expert mentors.
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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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