Machine Learning with R, 3/e
暫譯: 使用 R 的機器學習（第三版）

Brett Lantz

Machine Learning with R, 3/e

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商品描述

Key Features

  • Third edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.5 and beyond
  • Harness the power of R to build flexible, effective, and transparent machine learning models
  • Learn quickly with a clear, hands-on guide by experienced machine learning teacher and practitioner, 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, Third Edition provides a hands-on, 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 to uncover key insights, make new predictions, and visualize your findings.

This new 3rd edition updates the classic R data science book with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R.

What you will learn

  • Discover the origins of machine learning and how exactly a computer learns by example
  • Prepare your data for machine learning work with the R programming language
  • 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 ― the basis of deep learning
  • Avoid bias in machine learning models
  • Evaluate your models and improve their performance
  • Connect R to SQL databases and emerging big data technologies such as Spark, H2O, and TensorFlow

Who this book is for

Data scientists, students, and other practitioners who want a clear, accessible guide to machine learning with R.

商品描述(中文翻譯)

**主要特點**

- 第三版暢銷且廣受好評的 R 機器學習書籍，已更新並改進至 R 3.5 及以後版本
- 利用 R 的強大功能構建靈活、有效且透明的機器學習模型
- 由經驗豐富的機器學習教師及實踐者 Brett Lantz 提供清晰、實用的快速學習指南

**書籍描述**

機器學習的核心在於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，能快速且輕鬆地從數據中獲取洞見。

《使用 R 的機器學習》第三版提供了一本實用且易讀的指南，幫助您將機器學習應用於現實世界的問題。無論您是經驗豐富的 R 使用者還是新手，Brett Lantz 都會教您所需的一切，以揭示關鍵洞見、做出新預測並可視化您的發現。

這本全新的第三版更新了經典的 R 數據科學書籍，包含更新且更好的函式庫、關於機器學習中的倫理和偏見問題的建議，以及深度學習的介紹。從您的數據中發現強大的新洞見；與 R 一起探索機器學習。

**您將學到的內容**

- 探索機器學習的起源，以及計算機如何通過範例學習
- 使用 R 程式語言準備您的數據以進行機器學習工作
- 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並估算財務價值
- 使用人工神經網絡建模複雜過程——深度學習的基礎
- 避免機器學習模型中的偏見
- 評估您的模型並改善其性能
- 將 R 連接到 SQL 數據庫及新興的大數據技術，如 Spark、H2O 和 TensorFlow

**本書適合誰**

數據科學家、學生及其他希望獲得清晰、易於理解的 R 機器學習指南的實踐者。

作者簡介

Brett Lantz (@DataSpelunking) has spent more than 10 years using innovative data methods to understand human behavior. A sociologist by training, Brett was first captivated by machine learning during research on a large database of teenagers' social network profiles. Brett is a DataCamp instructor and a frequent speaker at machine learning conferences and workshops around the world. He is known to geek out about data science applications for sports, autonomous vehicles, foreign language learning, and fashion, among many other subjects, and hopes to one day blog about these subjects at Data Spelunking, a website dedicated to sharing knowledge about the search for insight in data.

作者簡介(中文翻譯)

Brett Lantz (@DataSpelunking) 在使用創新的數據方法理解人類行為方面已經有超過 10 年的經驗。作為一名受過訓練的社會學家，Brett 在研究一個大型青少年社交網絡資料庫時首次被機器學習所吸引。Brett 是 DataCamp 的講師，並且經常在全球的機器學習會議和工作坊上演講。他以對數據科學在體育、自動駕駛車輛、外語學習和時尚等多個主題的應用充滿熱情而聞名，並希望有一天能在 Data Spelunking 這個專注於分享數據洞察知識的網站上撰寫這些主題的博客。

目錄大綱

  1. Introducing Machine Learning
  2. Managing and Understanding Data
  3. Lazy Learning – Classification Using Nearest Neighbors
  4. Probabilistic Learning – Classification Using Naive Bayes
  5. Divide and Conquer – Classification Using Decision Trees and Rules
  6. Forecasting Numeric Data – Regression Methods
  7. Black Box Methods – Neural Networks and Support Vector Machines
  8. Finding Patterns – Market Basket Analysis Using Association Rules
  9. Finding Groups of Data – Clustering with k-means
  10. Evaluating Model Performance
  11. Improving Model Performance
  12. Specialized Machine Learning Topics

目錄大綱(中文翻譯)


  1. Introducing Machine Learning

  2. Managing and Understanding Data

  3. Lazy Learning – Classification Using Nearest Neighbors

  4. Probabilistic Learning – Classification Using Naive Bayes

  5. Divide and Conquer – Classification Using Decision Trees and Rules

  6. Forecasting Numeric Data – Regression Methods

  7. Black Box Methods – Neural Networks and Support Vector Machines

  8. Finding Patterns – Market Basket Analysis Using Association Rules

  9. Finding Groups of Data – Clustering with k-means

  10. Evaluating Model Performance

  11. Improving Model Performance

  12. Specialized Machine Learning Topics

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