Machine Learning with R

DL for Text Classification you own this product

This project is part of the liveProject series Machine Learning with R for Text Data
prerequisites
intermediate R • data splitting • feature engineering • fit models for multiclass outcomes • define a range of neural network model architectures • tune machine learning models • create local explanations for why a model generates specific predictions
skills learned
sample datasets for unbiased measures of model performance • feature engineering for text data • fit models using Keras • evaluate classification models using appropriate metrics • tune hyperparameters to maximize model performance • explain how a machine learning model generated specific predictions
Benjamin Soltoff
1 week · 4-6 hours per week · INTERMEDIATE
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Predict the future! You’re an academic researcher working on a project that predicts what policy areas the U.S. government will prioritize. To achieve your goal, you’ll train three kinds of deep learning neural networks on a legislation dataset (a CSV file containing one row for every bill introduced in the U.S. Congress). With the resulting text classifications, you’ll predict the area of focus for future policy bills.

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

Benjamin Soltoff
Benjamin Soltoff is an assistant senior instructional professor in computational social science at the University of Chicago. He’s the associate director of the Masters in Computational Social Science program and teaches courses in research design, programming in R, data visualization, and machine learning. He holds a PhD in political science from Pennsylvania State University. He develops training workshops for learners in academia and industry on data science techniques using R with an emphasis on reproducible workflows, and he’s an RStudio-certified trainer. For more information, you can view his personal site.

prerequisites

This liveProject is for intermediate R programmers who know the basics of data science. To begin these liveProjects you will need to be familiar with the following:

TOOLS
  • Intermediate R
TECHNIQUES
  • Data splitting
  • Feature engineering
  • Fit models for multiclass outcomes
  • Define a range of neural network model architectures
  • Tune machine learning models
  • Create local explanations for why a model generates specific predictions

you will learn

In this liveProject, you’ll learn to apply deep learning to supervised text classification.

  • Sample datasets for unbiased measures of model performance
  • Feature engineering for text data
  • Fit models using Keras
  • Evaluate classification models using appropriate metrics
  • Tune hyperparameters to maximize model performance
  • Explain how a machine learning model generated specific predictions

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
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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  • DL for Text Classification project for free

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  • DL for Text Classification project for free