Extending ML for Text Classification you own this product

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
Included with a Manning Online subscription
try now

pro $24.99 per month

  • access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
  • choose one free eBook per month to keep
  • exclusive 50% discount on all purchases

lite $19.99 per month

  • access to all Manning books, including MEAPs!

team

5, 10 or 20 seats+ for your team - learn more

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.

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

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.

related titles

choose your plan

pro

monthly
annual
$24.99
$249.99
only $20.83 per month
  • access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
  • choose another free product every time you renew
  • choose twelve free products per year
  • exclusive 50% discount on all purchases
  • Extending ML for Text Classification project for free

team

monthly
annual
$49.99
$499.99
only $41.67 per month
  • five seats for your team
  • access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
  • choose another free product every time you renew
  • choose twelve free products per year
  • exclusive 50% discount on all purchases
  • Extending ML for Text Classification project for free