Emil Hvitfeldt, Julia Silge

Supervised Machine Learning for Text Analysis in R

• Broschiertes Buch

Produktbeschreibung

This book is designed to provide practical guidance and directly applicable knowledge for data scientists and analysts who want to integrate text into their modeling pipelines. We assume that the reader is somewhat familiar with R, predictive modeling concepts for non-text data, and the tidyverse family of packages.

Produktdetails

• Chapman & Hall/CRC Data Science Series
• Verlag: Taylor & Francis Ltd
• Seitenzahl: 402
• Erscheinungstermin: 22. Oktober 2021
• Englisch
• Abmessung: 231mm x 152mm x 23mm
• Gewicht: 608g
• ISBN-13: 9780367554194
• ISBN-10: 0367554194
• Artikelnr.: 62151520

Autorenporträt

Emil Hvitfeldt is a clinical data analyst working in healthcare, and an adjunct professor at American University where he is teaching statistical machine learning with tidymodels. He is also an open source R developer and author of the textrecipes package. Julia Silge is a data scientist and software engineer at RStudio PBC where she works on open source modeling tools. She is an author, an international keynote speaker and educator, and a real-world practitioner focusing on data analysis and machine learning practice.

Inhaltsangabe

1. Language and modeling. 2. Tokenization. 3. Stop words. 4. Stemming. 5.
Word Embeddings. 6. Regression. 7. Classification. 8. Dense neural
networks. 9. Long short-term memory (LSTM) networks. 10. Convolutional
neural networks.