Leer en linea Hands-On Machine Learning with R (Chapman & Hall/CRC The R Series) de Brad Boehmke,Brandon M. Greenwell Libro PDF, ePub, Mobile, Hands-On Machine Learning with R (Chapman & Hall/CRC The R Series) Torrent
Hands-On Machine Learning with R (Chapman & Hall/CRC The R Series) de Brad Boehmke,Brandon M. Greenwell
Descripci贸n - Cr铆ticas 'Hands-On Machine Learning with R is a great resource for understanding and applying models. Each section provides descriptions and instructions using a wide range of R packages.' - Max Kuhn, Machine Learning Software Engineer, RStudio 'You can't find a better overview of practical machine learning methods implemented with R.'- JD Long, co-author of R Cookbook 'Simultaneously approachable, accessible, and rigorous, Hands-On Machine Learning with R offers a balance of theory and implementation that can actually bring you from relative novice to competent practitioner.' - Mara Averick, RStudio Dev Advocate Rese帽a del editor Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today鈥檚 most popular machine learning methods. This book serves as a practitioner鈥檚 guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory.聽 Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R鈥檚 machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: 路聽聽聽聽聽聽聽聽 Offers a practical and applied introduction to the most popular machine learning methods. 路聽聽聽聽聽聽聽聽 Topics covered include feature engineering, resampling, deep learning and more. 路聽聽聽聽聽聽聽聽 Uses a hands-on approach and real world data. Biograf铆a del autor Brad Boehmke is a data scientist at 84.51掳 where he wears both software developer and machine learning engineer hats. He is an Adjunct Professor at the University of Cincinnati, author of Data Wrangling with R, and creator of multiple public and private enterprise R packages.
Brandon Greenwell is a data scientist at 84.51掳 where he works on a diverse team to enable, empower, and encourage others to successfully apply machine learning to solve real business problems. He鈥檚 part of the Adjunct Graduate Faculty at Wright State University, an Adjunct Instructor at the University of Cincinnati, and the author of several R packages available on CRAN.
Detalles del Libro
- Name: Hands-On Machine Learning with R (Chapman & Hall/CRC The R Series)
- Autor: Brad Boehmke,Brandon M. Greenwell
- Categoria: Libros,Ciencias, tecnolog铆a y medicina,Matem谩ticas
- Tama帽o del archivo: 17 MB
- Tipos de archivo: PDF Document
- Descargada: 714 times
- Idioma: Espa帽ol
- Archivos de estado: AVAILABLE
Leer en linea Hands-On Machine Learning with R (Chapman & Hall/CRC The R Series) de Brad Boehmke,Brandon M. Greenwell Libro PDF, ePub, Mobile
Handson machine learning with r bradley boehmke the book favors a handson approach growing an intuitive understanding of machine learning through concrete examples and just a little bit of theory while you can read this book without opening r we highly recommend you experiment with the code examples provided throughout
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