Feature Engineering for Machine Learning Models: Principles and Techniques for Data Scientists
| Author | : | |
| Rating | : | 4.94 (817 Votes) |
| Asin | : | 1491953241 |
| Format Type | : | paperback |
| Number of Pages | : | 200 Pages |
| Publish Date | : | 2013-07-20 |
| Language | : | English |
DESCRIPTION:
Berkeley.. Previous roles include Director of Data Science at GraphLab/Dato/Turi, machine learning researcher at Microsoft Research, Redmond, and postdoctoral fellow at Carnegie Mellon University. Currently, she is a Senior Manager in 's Ad Platform. Her experience spans algorithm and platform development and applications. She received a Ph.D. in Electrical Engineering and Computer science, and B.A. degrees in Computer Science in Mathematics, all from U.C. About the AuthorAlice is a technical leader in the field of Machine Learning
Her experience spans algorithm and platform development and applications. Berkeley.. Previous roles include Director of Data Science at GraphLab/Dato/Turi, machine learning researcher at Microsoft Research, Redmond, and postdoctoral fellow at Carnegie Mellon University. degrees in Computer Science in Mathematics, all from U.C. in Electrical Engineering and Computer science, and B.A. She received a Ph.D.
Feature engineering is essential to applied machine learning, but using domain knowledge to strengthen your predictive models can be difficult and expensive. If you understand basic machine learning concepts like supervised and unsupervised learning, you’re ready to get started. 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. 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 wellUse common methods for different data types, including images, text, and logsUnderstand how different techniques such as feature scaling and principal component analysis workUnderstand how unsupervised feature learning wor