Feature engineering for machine learning (Record no. 7615)
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| 000 -LEADER | |
|---|---|
| fixed length control field | nam a22 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20181030142931.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 181029b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789352137114 |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | AIKTC-KRRC |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | ENG |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Edition number | DDC23 |
| Classification number | 006.31 |
| Item number | ZHE/CAS |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 4950 |
| Personal name | Zheng, Alice |
| 245 ## - TITLE STATEMENT | |
| Title | Feature engineering for machine learning |
| Remainder of title | : Principles and techniques for data scientists |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Navi Mumbai |
| Name of publisher, distributor, etc. | |
| Date of publication, distribution, etc. | 2018 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xii, 200p. |
| Other physical details | | Binding - Paperback | |
| Dimensions | 23*17.9 cm |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, youll learn techniques for extracting and transforming featuresthe numeric representations of raw datainto 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. |
| Expansion of summary note | Youll examine: Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transforms Natural text techniques: bag-of-words, n-grams, and phrase detection Frequency-based filtering and feature scaling for eliminating uninformative features Encoding techniques of categorical variables, including feature hashing and bin-counting Model-based feature engineering with principal component analysis The concept of model stacking, using k-means as a featurization technique Image feature extraction with manual and deep-learning techniques |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4619 |
| Topical term or geographic name entry element | EXTC Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 4952 |
| Personal name | Casari, Amanda |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Text Books |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Permanent Location | Current Location | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Full call number | Barcode | Date last seen | Cost, replacement price | Price effective from | Koha item type |
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| Not For Loan | Reference | School of Engineering & Technology | School of Engineering & Technology | Reference Section | 2018-10-30 | 2 | 500.00 | 006.31 ZHE/CAS | E14534 | 2024-06-29 | 625.00 | 2018-10-30 | Text Books |