Machine Learning with R

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 • fitting models for multiclass outcomes • tuning machine learning models
skills learned
resample datasets for unbiased measures of model performance • feature engineering for text data • fit models using a tidy framework • evaluate classification models using appropriate metrics • tune machine learning models to maximize their effectiveness
Benjamin Soltoff
1 week · 4-6 hours per week · INTERMEDIATE
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liveProject This project is part of the liveProject series Machine Learning with R for Text Data 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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Look inside

Play the role of an academic researcher preparing a machine learning model to predict the U.S. government’s focus for new policy legislation. You’ll process the legislation dataset with resampling and feature engineering techniques, employ a range of algorithms, including penalized regression and XGBoost, to fit a series of ML models, evaluate the effectiveness of the models, and tune them accordingly.

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
  • Fitting models for multiclass outcomes
  • Tuning machine learning models

you will learn

In this liveProject, you’ll learn to estimate machine learning models based on text data using the tidymodels framework.

  • Resample datasets for unbiased measures of model performance
  • Feature engineering for text data
  • Fit models using a tidy framework
  • Evaluate classification models using appropriate metrics
  • Tune machine learning models to maximize their effectiveness

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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