Publisher |
Shroff/O'Reilly |
Publication Year |
2018 |
ISBN-13 |
9789352137114 |
ISBN-10 |
9789352137114 |
Binding |
Paperback |
Number of Pages |
184 Pages |
Language |
(English) |
Dimensions (Cms) |
20X14X4 |
Weight (grms) |
350 |
Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. with this practical book, you and rsquo;ll learn techniques for extracting and transforming features and mdash;the numeric representations of raw dataóinto formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering.
Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn and Matplotlib are used in code examples.
Alice Zheng
Alice Zheng is a technical leader in the field of Machine Learning. Her experience spans algorithm and platform development and applications. Currently, she is a Senior Manager in Amazon's Ad Platform. 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. She received a Ph.D. in Electrical Engineering and Computer science and B.A. degrees in Computer Science in Mathematics, all from U.C. Berkeley.
Alice Zheng
Shroff/O'Reilly