Paperback. Machine Learning with R, tidyverse, and mlr teaches readers how to gain valuable insights from their data using the powerful R programming language. In his engaging and informal style, author and R expert Hefin Ioan Rhys lays a firm foundation of ML basics and introduces readers to the tidyverse, a powerful set of R tools designed specifically for practical data science. Key Features Commonly used ML techniques Using the tidyverse packages to organize and plot your data Validating model performance Choosing the best ML model for your task A variety of hands-on coding exercises ML best practices For readers with basic programming skills in R, Python, or another standard programming language. About the technology Machine learning techniques accurately and efficiently identify patterns and relationships in data and use those models to make predictions about new data. ML techniques can work on even relatively small datasets, making these skills a powerful ally for nearly any data analysis task. Hefin Ioan Rhysis a senior laboratory research scientist in the Flow Cytometry Shared Technology Platform at The Francis Crick Institute. He spent the final year of his PhD program teaching basic R skills at the university. A data science and machine learning enthusiast, he has his own Youtube channel featuring screencast tutorials in R and R Studio. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

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Detalles bibliogr�ficos

T�tulo: Machine Learning with R, tidyverse, and mlr (Paperback)
Editor: Manning Publications, New York
Fecha de publicaci�n: 2020
Idioma: Ingl�s
ISBN 10: 1617296570
ISBN 13: 9781617296574
Encuadernaci�n: Paperback
Estado: new

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Sinopsis

Machine Learning with R, tidyverse, and mlr teaches readers how to gain valuable insights from their data using the powerful R programming language.

In his engaging and informal style, author and R expert Hefin Ioan Rhys lays a firm foundation of ML basics and introduces readers to the tidyverse, a powerful set of R tools designed specifically for practical data science.

Key Features

� Commonly used ML techniques

� Using the tidyverse packages to organize and plot your data

� Validating model performance

� Choosing the best ML model for your task

� A variety of hands-on coding exercises

� ML best practices

For readers with basic programming skills in R, Python, or another standard programming language.

About the technology

Machine learning techniques accurately and efficiently identify patterns and relationships in data and use those models to make predictions about new data. ML techniques can work on even relatively small datasets, making these skills a powerful ally for nearly any data analysis task.

Hefin Ioan Rhysis a senior laboratory research scientist in the Flow Cytometry Shared Technology Platform at The Francis Crick Institute. He spent the final year of his PhD program teaching basic R skills at the university. A data science and machine learning enthusiast, he has his own Youtube channel featuring screencast tutorials in R and R Studio.

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