Machine Learning for Hackers (Paperback)
暫譯: 駭客的機器學習 (平裝本)

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)

如果您是一位有經驗的程式設計師，對數據分析感興趣，本書將幫助您入門機器學習——這是一套算法工具，能讓計算機自我訓練以自動化有用的任務。作者 Drew Conway 和 John Myles White 通過一系列實作案例研究，幫助您理解機器學習和統計工具，而不是傳統的數學重點介紹。

每一章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，您將學習如何分析樣本數據集並編寫簡單的機器學習算法。《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。

- 開發一個天真的貝葉斯分類器，僅根據電子郵件的文本來判斷其是否為垃圾郵件
- 使用線性回歸預測前 1,000 個網站的頁面瀏覽量
- 通過嘗試破解一個簡單的字母密碼來學習優化技術
- 根據美國參議員的投票記錄進行統計比較
- 從 Twitter 數據構建一個“跟隨誰”的推薦系統

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