MARC details
| 000 -LEADER |
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| fixed length control field |
03313nam a22004335i 4500 |
| 001 - CONTROL NUMBER |
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| control field |
ssj0002239883 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
WaSeSS |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20220420141943.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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m d |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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cr n |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
191001s2019 xxu| o |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781484249468 |
| 024 7# - OTHER STANDARD IDENTIFIER |
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| Standard number or code |
10.1007/978-1-4842-4947-5 |
| Source of number or code |
doi |
| 040 ## - CATALOGING SOURCE |
|---|
| Modifying agency |
WaSeSS |
| Transcribing agency |
AIMIT LIBRARY |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
Q334-342 |
| 072 #7 - SUBJECT CATEGORY CODE |
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| Subject category code |
UYQ |
| Source |
bicssc |
| 072 #7 - SUBJECT CATEGORY CODE |
|---|
| Subject category code |
COM004000 |
| Source |
bisacsh |
| 072 #7 - SUBJECT CATEGORY CODE |
|---|
| Subject category code |
UYQ |
| Source |
thema |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.31 |
| Edition number |
2 |
| Item number |
SWAM |
| 092 ## - LOCALLY ASSIGNED DEWEY CALL NUMBER (OCLC) |
|---|
| Classification number |
EBOOK |
| 999 ## - SYSTEM CONTROL NUMBERS (KOHA) |
|---|
| Koha Dewey Subclass [OBSOLETE] |
03655696 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Swamynathan, Manohar. |
| 9 (RLIN) |
30557 |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Mastering Machine Learning with Python in Six Steps : |
| Remainder of title |
a Practical Implementation Guide to Predictive Data Analytics Using Python / |
| Statement of responsibility, etc. |
by Manohar Swamynathan. |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
2nd ed. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Berkeley, CA : |
| Name of publisher, distributor, etc. |
Apress : |
| -- |
Imprint: Apress, |
| Date of publication, distribution, etc. |
2019. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xvii, 457p. |
| Dimensions |
25.3 cm. |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Chapter 1: Step 1 - Getting Started with Python -- Chapter 2 : Step 2 - Introduction to Machine Learning -- Chapter 3: Step 3 - Fundamentals of Machine Learning -- Chapter 4: Step 4 - Model Diagnosis and Tuning -- Chapter 5: Step 5 - Text Mining, NLP AND Recommender Systems -- Chapter 6: Step 6 - Deep and Reinforcement Learning -- Chapter 7 : Conclusion. |
| 506 ## - RESTRICTIONS ON ACCESS NOTE |
|---|
| Terms governing access |
Requires an SPL library card. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Explore fundamental to advanced Python 3 topics in six steps, all designed to make you a worthy practitioner. This updated version's approach is based on the "six degrees of separation" theory, which states that everyone and everything is a maximum of six steps away and presents each topic in two parts: theoretical concepts and practical implementation using suitable Python 3 packages. You'll start with the fundamentals of Python 3 programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as exploratory analysis, feature dimension reduction, regressions, time series forecasting and their efficient implementation in Scikit-learn are covered as well. You'll also learn commonly used model diagnostic and tuning techniques. These include optimal probability cutoff point for class creation, variance, bias, bagging, boosting, ensemble voting, grid search, random search, Bayesian optimization, and the noise reduction technique for IoT data. Finally, you'll review advanced text mining techniques, recommender systems, neural networks, deep learning, reinforcement learning techniques and their implementation. All the code presented in the book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage. |
| 538 ## - SYSTEM DETAILS NOTE |
|---|
| System details note |
Mode of access: World Wide Web. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Artificial intelligence |
| 9 (RLIN) |
30558 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Big data |
| 9 (RLIN) |
30559 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Open source software |
| 9 (RLIN) |
30560 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Computer programming |
| 9 (RLIN) |
30561 |
| 655 #7 - INDEX TERM--GENRE/FORM |
|---|
| Genre/form data or focus term |
Electronic books. |
| Source of term |
local |
| 9 (RLIN) |
30562 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Link text |
View this electronic item in O'Reilly Online Learning: Academic/Public Library Edition. |
| Uniform Resource Identifier |
<a href="https://ezproxy.spl.org/login?url=https://learning.oreilly.com/library/view/~/9781484249475/?ar">https://ezproxy.spl.org/login?url=https://learning.oreilly.com/library/view/~/9781484249475/?ar</a> |
| Public note |
An e-book available through full-text database. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Book |
| Edition |
2nd. |
| Call number prefix |
006.31 SWAM |
