Machine Learning with R - Fourth Edition: Learn techniques for building and improving machine learning models, from data preparation to model tuning,

Learn how to solve real-world data problems using machine learning and R

Key Features:

• The 10th Anniversary Edition of the bestselling R machine learning book, updated with 50% new content for R 4.0.0 and beyond
• Harness the power of R to build flexible, effective, and transparent machine learning models
• Learn quickly with this clear, hands-on guide by machine learning expert Brett Lantz

Book Description:

Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data.

Machine Learning with R, Fourth Edition provides a hands-on, accessible, and readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need for data pre-processing, uncovering key insights, making new predictions, and visualizing your findings. This 10th Anniversary Edition features several new chapters that reflect the progress of ML in the last few years and help you build your data science skills and tackle more challenging problems, including making successful ML models and advanced data preparation, building better learners, and making use of big data.

You'll also find updates to the classic R data science book to R 4.0.0 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Whether you're looking to take your first steps with R for machine learning or making sure your skills and knowledge are up to date, this is an unmissable read that will help you find powerful new insights in your data.

What You Will Learn:

• Learn the end-to-end process of machine learning from raw data to implementation
• Classify important outcomes using nearest neighbor and Bayesian methods
• Predict future events using decision trees, rules, and support vector machines
• Forecast numeric data and estimate financial values using regression methods
• Model complex processes with artificial neural networks
• Prepare, transform, and clean data using the tidyverse
• Evaluate your models and improve their performance
• Connect R to SQL databases and emerging big data technologies such as Spark, Hadoop, H2O, and TensorFlow

Who this book is for:

This book is designed to help data scientists, actuaries, data analysts, financial analysts, social scientists, business and machine learning students, and any other practitioners who want a clear, accessible guide to machine learning with R. No R experience is required, although prior exposure to statistics and programming is helpful.

學習如何使用機器學習和R解決真實世界的數據問題

重點特色：
- 畅销的R機器學習書籍的第十周年版，更新了50％的新內容，適用於R 4.0.0及更高版本
- 利用R的強大功能構建靈活、有效和透明的機器學習模型
- 由機器學習專家Brett Lantz提供的清晰、實用的指南，讓您快速上手

書籍描述：
機器學習的核心是將數據轉化為可行的知識。R提供了一套強大的機器學習方法，可以快速且輕鬆地從數據中獲取洞察力。

《Machine Learning with R, Fourth Edition》提供了一個實用、易於理解且易於閱讀的指南，教您如何將機器學習應用於真實世界的問題。無論您是有經驗的R用戶還是初學者，Brett Lantz都會教您進行數據預處理、發現關鍵洞察、進行新預測和可視化結果所需的一切。這本第十周年版新增了幾個章節，反映了機器學習在過去幾年中的進展，幫助您建立數據科學技能，解決更具挑戰性的問題，包括成功構建機器學習模型和高級數據準備、構建更好的學習器以及利用大數據。

您還將找到關於經典R數據科學書籍的更新，適用於R 4.0.0的新且更好的庫，關於機器學習中的道德和偏見問題的建議，以及深度學習的介紹。無論您是想要初次接觸R進行機器學習，還是確保自己的技能和知識是最新的，這本書都是一本不可錯過的閱讀，將幫助您在數據中找到強大的新洞察力。

學到什麼：
- 從原始數據到實施的機器學習的端到端過程
- 使用最近鄰和貝葉斯方法對重要結果進行分類
- 使用決策樹、規則和支持向量機預測未來事件
- 使用回歸方法預測數值數據和估計金融價值
- 使用人工神經網絡對複雜過程進行建模
- 使用tidyverse準備、轉換和清理數據
- 評估模型並提高其性能
- 將R連接到SQL數據庫和新興的大數據技術，如Spark、Hadoop、H2O和TensorFlow

適合對象：
本書旨在幫助數據科學家、精算師、數據分析師、金融分析師、社會科學家、商業和機器學習學生以及其他希望獲得清晰、易於理解的機器學習指南的從業人員。不需要R經驗，但具有統計和編程的先備知識會有所幫助。