MARC details
| 000 -LEADER |
|---|
| fixed length control field |
03329nam a22004457i 4500 |
| 001 - CONTROL NUMBER |
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| control field |
45959 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
BD-DhAAL |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20260621160624.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
260621t20182018caua e 001 0 eng d |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER |
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| LC control number |
2018276483 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
1491963042 |
|
|---|
| International Standard Book Number |
9781491963043 |
| 035 ## - SYSTEM CONTROL NUMBER |
|---|
| System control number |
21019700 |
|
|---|
| System control number |
(OCoLC)962257016 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
YDX |
| Language of cataloging |
eng |
| Transcribing agency |
YDX |
| Description conventions |
rda |
| Modifying agency |
OCLCQ |
| -- |
BTCTA |
| -- |
GK8 |
| -- |
SINLB |
| -- |
JRZ |
| -- |
BDX |
| -- |
UUM |
| -- |
GP5 |
| -- |
OCLCF |
| -- |
CLE |
| -- |
FIE |
| -- |
COD |
| -- |
OCLCQ |
| -- |
DLC |
| -- |
BD-DhAAL |
| 042 ## - AUTHENTICATION CODE |
|---|
| Authentication code |
lccopycat |
| 050 00 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
QA76.73.P98 |
| Item number |
B454 2018 |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.35 |
| Edition number |
23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Bengfort, Benjamin, |
| Dates associated with a name |
1984- |
| Relator term |
author. |
| 9 (RLIN) |
61437 |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Applied text analysis with Python : |
| Remainder of title |
enabling language-aware data products with machine learning / |
| Statement of responsibility, etc |
Benjamin Bengfort, Rebecca Bilbro, and Tony Ojeda. |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
First edition. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Sebastopol, CA : |
| Name of publisher, distributor, etc |
O'Reilly Media, Inc., |
| Date of publication, distribution, etc |
2018. |
| 264 #4 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT.REV) |
|---|
| Date of publication, distribution,etc |
©2018 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xviii, 310 pages : |
| Other physical details |
illustrations ; |
| Dimensions |
25 cm |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Includes index. |
| 505 00 - FORMATTED CONTENTS NOTE |
|---|
| Title |
Language and computation -- |
| -- |
Building a custom corpus -- |
| -- |
Corpus preprocessing and wrangling -- |
| -- |
Text vectorization and transformation pipelines -- |
| -- |
Classification for text analysis -- |
| -- |
Clustering for text similarity -- |
| -- |
Context-aware text analysis -- |
| -- |
Text visualization -- |
| -- |
Graph analysis of text -- |
| -- |
Chatbots -- |
| -- |
Scaling text analytics with multiprocessing and Spark -- |
| -- |
Deep learning and beyond. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural language is through the creative application of text analytics. This practical book presents a data scientist's approach to building language-aware products with applied machine learning. You will learn robust, repeatable, and scalable techniques for text analysis with Python, including contextual and linguistic feature engineering, vectorization, classification, topic modeling, entity resolution, graph analysis, and visual steering. By the end of the book, you'll be equipped with practical methods to solve any number of complex real-world problems.- Preprocess and vectorize text into high-dimensional feature representations - Perform document classification and topic modeling - Steer the model selection process with visual diagnostics - Extract key phrases, named entities, and graph structures to reason about data in text - Build a dialog framework to enable chatbots and language-driven interaction - Use Spark to scale processing power and neural networks to scale model complexity.-- |
| 526 8# - STUDY PROGRAM INFORMATION NOTE |
|---|
| Program name |
AAL |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Natural language processing (Computer science) |
|
|---|
| Topical term or geographic name as entry element |
Python (Computer program language) |
|
|---|
| Topical term or geographic name as entry element |
Machine learning. |
|
|---|
| Topical term or geographic name as entry element |
Machine learning. |
|
|---|
| Topical term or geographic name as entry element |
Natural language processing (Computer science) |
|
|---|
| Topical term or geographic name as entry element |
Python (Computer program language) |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Bilbro, Rebecca, |
| Relator term |
author. |
| 9 (RLIN) |
61438 |
|
|---|
| Personal name |
Ojeda, Tony, |
| Relator term |
author. |
| 9 (RLIN) |
61439 |
| 852 1# - LOCATION/CALL NUMBER |
|---|
| Location |
Ayesha Abed Library |
| Shelving location |
General Stacks |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Item type |
Book |
