Supervised Machine Learning for Text Analysis in R (Chapman & Hall/CRC Data Science Series) free ebook download.

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Supervised Machine Learning for Text Analysis in R (Chapman & Hall/CRC Data Science Series) free ebook download

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Mathematics

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Author(s):	Emil Hvitfeldt

Date:	2021	Format:	rar	Language:	English	ISBN/ASIN:	0367554194
Pages:	402	OCR:		Quality:		ISBN13:
Uploader:	maveriks	Upload Date:	10/31/2021 4:23:37 AM

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0367554194 rar
Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text data for modeling, train models, and evaluate model performance using tools from the tidyverse and tidymodels ecosystem. Models like these can be used to make predictions for new observations, to understand what natural language features or characteristics contribute to differences in the output, and more. If you are already familiar with the basics of predictive modeling, use the comprehensive, detailed examples in this book to extend your skills to the domain of natural language processing.
This book provides practical guidance and directly applicable knowledge for data scientists and analysts who want to integrate unstructured text data into their modeling pipelines. Learn how to use text data for both regression and classification tasks, and how to apply more straightforward algorithms like regularized regression or support vector machines as well as deep learning approaches. Natural language must be dramatically transformed to be ready for computation, so we explore typical text preprocessing and feature engineering steps like tokenization and word embeddings from the ground up. These steps influence model results in ways we can measure, both in terms of model metrics and other tangible consequences such as how fair or appropriate model results are.

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3047194	Book	https://nitro.download/view/8F61D0FB2AC8...		0.00	maveriks

Tags: Analysis Chapman Data Hall Learning Machine Science Supervised Text

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