Machine Learning with R

Extending ML 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 using hashes and word embeddings • fit models for multiclass outcomes • tune machine learning models
skills learned
resample datasets • generate feature hashes for categorical variables • implement pre-trained word embeddings in a machine learning workflow • subsample an unbalanced dataset • evaluate classification models • explain how a machine learning model generates specific predictions
Benjamin Soltoff
1 week · 4-6 hours per week · INTERMEDIATE
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Look inside

Imagine you’re an academic researcher working on a project for predicting trends in the U.S. government’s policy-making priorities. Using modern techniques for text data feature engineering, you’ll fit a set of models, subsample the training data to minimize bias, evaluate the models’ performance using a test-set of observations, and leverage a tidy workflow to explain how a model generates specific predictions.

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 and have used the tidymodels framework for creating ML models. To begin these liveProjects you will need to be familiar with the following:

TOOLS
  • Intermediate R
TECHNIQUES
  • Data splitting
  • Feature engineering using hashes and word embeddings
  • Fit models for multiclass outcomes
  • Tune machine learning models

you will learn

In this liveProject, you’ll be adept at using multiple ML feature engineering techniques, subsampling procedures, and model explanation using the tidymodels framework.

  • Resample datasets for unbiased measures of model performance
  • Generate feature hashes for categorical variables
  • Implement pre-trained word embeddings in a machine learning workflow
  • Subsample an unbalanced dataset to minimize bias
  • Evaluate classification models using appropriate metrics
  • Explain how a machine learning model generates 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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  • Extending ML for Text Classification project for free

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  • access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
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  • choose twelve free products per year
  • exclusive 50% discount on all purchases
  • Extending ML for Text Classification project for free