Python 3 and Feature Engineering (notice n° 78073)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02364cam a2200277zu 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | FRCYB88949028 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250108002826.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250108s2023 fr | o|||||0|0|||eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781683929482 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | FRCYB88949028 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | FR-PaCSA |
| Language of cataloging | en |
| Transcribing agency | |
| Description conventions | rda |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Campesato, Oswald |
| 245 01 - TITLE STATEMENT | |
| Title | Python 3 and Feature Engineering |
| Statement of responsibility, etc. | ['Campesato, Oswald'] |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Name of producer, publisher, distributor, manufacturer | Mercury Learning and Information |
| Date of production, publication, distribution, manufacture, or copyright notice | 2023 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | p. |
| 336 ## - CONTENT TYPE | |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type code | c |
| Source | rdamdedia |
| 338 ## - CARRIER TYPE | |
| Carrier type code | c |
| Source | rdacarrier |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | This book is designed for data scientists, machine learning practitioners, and anyone with a foundational understanding of Python 3.x. In the evolving field of data science, the ability to manipulate and understand datasets is crucial. The book offers content for mastering these skills using Python 3. The book provides a fast-paced introduction to a wealth of feature engineering concepts, equipping readers with the knowledge needed to transform raw data into meaningful information. Inside, you’ll find a detailed exploration of various types of data, methodologies for outlier detection using Scikit-Learn, strategies for robust data cleaning, and the intricacies of data wrangling. The book further explores feature selection, detailing methods for handling imbalanced datasets, and gives a practical overview of feature engineering, including scaling and extraction techniques necessary for different machine learning algorithms. It concludes with a treatment of dimensionality reduction, where you’ll navigate through complex concepts like PCA and various reduction techniques, with an emphasis on the powerful Scikit-Learn framework.FEATURESIncludes numerous practical examples and partial code blocks that illuminate the path from theory to applicationExplores everything from data cleaning to the subtleties of feature selection and extraction, covering a wide spectrum of feature engineering topicsOffers an appendix on working with the “awk” command-line utilityFeatures companion files available for downloading with source code, datasets, and figures |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Campesato, Oswald |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Access method | Cyberlibris |
| Uniform Resource Identifier | <a href="https://international.scholarvox.com/netsen/book/88949028">https://international.scholarvox.com/netsen/book/88949028</a> |
| Electronic format type | text/html |
| Host name | |
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