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

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

作者: Alice Zheng Amanda Casari
出版社: O'Reilly
出版在: 2018-04-20
ISBN-13: 9781491953242
ISBN-10: 1491953241
裝訂格式: Paperback
總頁數: 218 頁





內容描述


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




相關書籍

Python 程式設計 ─ AI 與資料科學應用, 2/e

作者 劉立民

2018-04-20

網絡數據採集技術 — Java 網絡爬蟲實戰

作者 錢洋 薑元春

2018-04-20

定量數據分析( Analysing Quantitative Data for Business and Management Students)

作者 (美)查爾斯 A.謝爾巴姆

2018-04-20