Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists

Alice Zheng, Amanda Casari

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商品描述

Feature engineering is essential to applied machine learning, but using domain knowledge to strengthen your predictive models can be difficult and expensive. To help fill the information gap on feature engineering, this complete hands-on guide teaches beginning-to-intermediate data scientists how to work with this widely practiced but little discussed topic.

Author Alice Zheng explains common practices and mathematical principles to help engineer features for new data and tasks. If you understand basic machine learning concepts like supervised and unsupervised learning, you’re ready to get started. Not only will you learn how to implement feature engineering in a systematic and principled way, you’ll also learn how to practice better data science.

  • Learn exactly what feature engineering is, why it’s important, and how to do it well
  • Use common methods for different data types, including images, text, and logs
  • Understand how different techniques such as feature scaling and principal component analysis work
  • Understand how unsupervised feature learning works in the case of deep learning for images

商品描述(中文翻譯)

功能工程對於應用機器學習至關重要,但利用領域知識來增強預測模型可能會很困難且昂貴。為了填補有關功能工程的資訊差距,這本完整的實踐指南教導初學者至中級的資料科學家如何處理這個廣泛實踐但鮮少討論的主題。

作者Alice Zheng解釋了常見的實踐方法和數學原理,以幫助為新數據和任務工程特徵。如果您了解監督和非監督學習等基本機器學習概念,您就準備好開始了。您不僅將學習如何以系統和原則的方式實施功能工程,還將學習如何更好地實踐資料科學。

- 瞭解功能工程的確切定義、重要性以及如何做得好
- 使用不同數據類型的常見方法,包括圖像、文本和日誌
- 瞭解不同技術(如特徵縮放和主成分分析)的工作原理
- 瞭解在圖像深度學習的情況下非監督特徵學習的工作原理