Machine Learning with R, 3/e (R語言機器學習（第三版）)

Brett Lantz

Machine Learning with R, 3/e

買這商品的人也買了...

相關主題

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)

《R機器學習》第三版

主要特點


  • 暢銷書《R機器學習》的第三版，針對R 3.5及更高版本進行了更新和改進

  • 利用R的強大功能構建靈活、有效和透明的機器學習模型

  • 由經驗豐富的機器學習教師和實踐者Brett Lantz提供清晰、實用的指南，快速學習

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。

《R機器學習》第三版提供了一個實用、易讀的指南，教你如何將機器學習應用於現實世界的問題。無論你是一個有經驗的R用戶還是對這門語言新手，Brett Lantz都會教你一切，幫助你發現關鍵洞察、做出新的預測並可視化你的發現。

這本全新的第三版通過使用更新且更好的庫、提供機器學習中的道德和偏見問題建議，以及介紹深度學習，對經典的R數據科學書籍進行了更新。在你的數據中發現強大的新洞察力，探索R機器學習。

你將學到什麼


  • 了解機器學習的起源，以及計算機如何通過示例學習

  • 使用R編程語言為機器學習準備數據

  • 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（深度學習的基礎）對複雜過程進行建模

  • 避免機器學習模型中的偏見

  • 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、H2O和TensorFlow

適合閱讀對象

數據科學家、學生和其他希望獲得關於使用R進行機器學習的清晰、易於理解指南的從業人員。

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）已經花了超過10年的時間使用創新的數據方法來理解人類行為。作為一名社會學家，Brett在對一個龐大的青少年社交網絡資料庫進行研究期間，首次被機器學習所吸引。Brett是DataCamp的講師，並經常在世界各地的機器學習會議和研討會上演講。他以對運動、自動駕駛車輛、外語學習和時尚等眾多主題的數據科學應用感到興奮，並希望有一天能在Data Spelunking這個致力於分享有關在數據中尋找洞察力的知識的網站上撰寫博客。

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)

- 介紹機器學習
- 管理和理解資料
- 懶惰學習 - 使用最近鄰居進行分類
- 概率學習 - 使用朴素貝葉斯進行分類
- 分而治之 - 使用決策樹和規則進行分類
- 預測數值資料 - 迴歸方法
- 黑盒方法 - 神經網絡和支持向量機
- 尋找模式 - 使用關聯規則進行市場籃分析
- 尋找資料群組 - 使用k-means進行分群
- 評估模型性能
- 提升模型性能
- 專門的機器學習主題

類似商品