Machine Learning for Hackers (Paperback) (駭客的機器學習)

Drew Conway, John Myles White

Machine Learning for Hackers (Paperback)

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商品描述

If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.

Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research.

  • Develop a naïve Bayesian classifier to determine if an email is spam, based only on its text
  • Use linear regression to predict the number of page views for the top 1,000 websites
  • Learn optimization techniques by attempting to break a simple letter cipher
  • Compare and contrast U.S. Senators statistically, based on their voting records
  • Build a “whom to follow” recommendation system from Twitter data

商品描述(中文翻譯)

如果你是一位有經驗的程式設計師，對於處理數據感興趣，這本書將帶領你進入機器學習的世界，這是一套能夠讓電腦自動訓練自己以執行有用任務的演算法工具。作者Drew Conway和John Myles White透過一系列實際案例研究，幫助你理解機器學習和統計工具，而不是傳統的數學重視的介紹方式。

每一章節都專注於機器學習中的特定問題，例如分類、預測、優化和推薦。使用R程式語言，你將學習如何分析樣本數據集並編寫簡單的機器學習演算法。《Machine Learning for Hackers》非常適合來自各種背景的程式設計師，包括商業、政府和學術研究。

以下是書中的一些案例：
- 使用一個簡單的貝葉斯分類器來判斷一封郵件是否為垃圾郵件，僅基於其內容文字
- 使用線性回歸來預測前1000個網站的瀏覽量
- 嘗試破解一個簡單的字母密碼，以學習優化技巧
- 根據他們的投票記錄，統計比較美國參議員之間的差異
- 從Twitter數據中建立一個「推薦關注對象」系統

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