---
title: "Brad Boehmke og Brandon M. Greenwell Hands-On Machine Learning with R"
ean: "9781138495685"
brand: "Brad Boehmke og Brandon M. Greenwell"
category: "Medier > Bøger"
price_min: 954
price_max: 954
currency: DKK
vendor_count: 1
in_stock: true
url: https://pricetracker.dk/p/9781138495685/hands-on-machine-learning-with-r
last_checked: 2026-10-14
---

# Brad Boehmke og Brandon M. Greenwell Hands-On Machine Learning with R

EAN 9781138495685 · Mærke: Brad Boehmke og Brandon M. Greenwell · Kategori: Medier > Bøger

Sammenlign priser på Brad Boehmke og Brandon M. Greenwell Hands-On Machine Learning with R fra 1 forhandler. Billigste pris nu: 954 kr hos Saxo DK. Se aktuelle priser og køb hos den billigste forhandler på https://pricetracker.dk/p/9781138495685/hands-on-machine-learning-with-r

## Priser
- **Billigste pris nu:** 954 kr hos Saxo DK
- **Prisspænd:** 954 kr – 954 kr på tværs af 1 forhandler

## Forhandlere
| Forhandler | Pris | Levering |
|---|---|---|
| Saxo DK | 954 kr | — |

## Om produktet
Beskrivelse fra forhandleren Saxo DK:

> 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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Priserne er sidst kontrolleret 2026-10-14.