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Hands-On Machine Learning with R

Af: Brad Boehmke, Brandon M. Greenwell Engelsk Hardback

Hands-On Machine Learning with R

Af: Brad Boehmke, Brandon M. Greenwell Engelsk Hardback
Tjek vores konkurrenters priser

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s 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. 

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’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results.

Features:

·         Offers a practical and applied introduction to the most popular machine learning methods.

·         Topics covered include feature engineering, resampling, deep learning and more.

·         Uses a hands-on approach and real world data.

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Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s 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. 

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’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results.

Features:

·         Offers a practical and applied introduction to the most popular machine learning methods.

·         Topics covered include feature engineering, resampling, deep learning and more.

·         Uses a hands-on approach and real world data.

Produktdetaljer
Sprog: Engelsk
Sider: 484
ISBN-13: 9781138495685
Indbinding: Hardback
Udgave:
ISBN-10: 1138495689
Udg. Dato: 11 nov 2019
Længde: 31mm
Bredde: 243mm
Højde: 160mm
Forlag: Taylor & Francis Ltd
Oplagsdato: 11 nov 2019
Forfatter(e) Brad Boehmke, Brandon M. Greenwell


Kategori Økonometri og økonomisk statistik


ISBN-13 9781138495685


Sprog Engelsk


Indbinding Hardback


Sider 484


Udgave


Længde 31mm


Bredde 243mm


Højde 160mm


Udg. Dato 11 nov 2019


Oplagsdato 11 nov 2019


Forlag Taylor & Francis Ltd

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