Hands-On Machine Learning with R (Hardcover)

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.

《實戰機器學習與 R》提供了一種實用且應用導向的學習方法，讓讀者對當今最流行的機器學習方法有直觀的理解。本書旨在幫助讀者學習如何在 R 中應用機器學習技術，包括使用各種 R 套件（如 glmnet、h2o、ranger、xgboost、keras 等）來有效地建模並從數據中獲取洞察。本書偏重於實踐，通過具體的例子和少量理論，提供對機器學習概念的直觀理解。

本書全面介紹了機器學習過程，包括特徵工程、重抽樣、超參數調整、模型評估和解釋。讀者將接觸到強大的算法，如正則化回歸、隨機森林、梯度提升機、深度學習、廣義低秩模型等。通過實踐和使用真實世界的數據，讀者將對驅動這些算法和套件的架構和引擎有直觀的理解，了解何時以及如何調整各種超參數，並能夠解釋模型結果。通過閱讀本書，讀者應該能夠熟練掌握 R 的機器學習技術堆棧，並能夠實施系統化的方法來產生高質量的建模結果。

特點：
- 提供了一個實用且應用導向的機器學習方法介紹。
- 涵蓋的主題包括特徵工程、重抽樣、深度學習等。
- 使用實踐方法和真實世界的數據。

Brad Boehmke is a data scientist at 84.51° 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° where he works on a diverse team to enable, empower, and encourage others to successfully apply machine learning to solve real business problems. He's 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.

Brad Boehmke是84.51度的資料科學家，他同時擔任軟體開發人員和機器學習工程師的角色。他是辛辛那提大學的兼任教授，也是《Data Wrangling with R》的作者，並創建了多個公共和私人企業R套件。

Brandon Greenwell是84.51度的資料科學家，他在一個多元化的團隊中工作，致力於使他人能夠成功應用機器學習解決實際的業務問題。他是萊特州立大學的兼任研究生教職員，辛辛那提大學的兼任講師，並且是幾個在CRAN上可用的R套件的作者。