MARC details
| 000 -LEADER |
|---|
| fixed length control field |
04585cam a22005415i 4500 |
| 001 - CONTROL NUMBER |
|---|
| control field |
21937178 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20220425114538.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
|---|
| fixed length control field |
m |o d | |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
|---|
| fixed length control field |
cr ||||||||||| |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
180630s2018 xxu|||| o |||| 0|eng |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER |
|---|
| LC control number |
2019768202 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781484235645 |
| 024 7# - OTHER STANDARD IDENTIFIER |
|---|
| Standard number or code |
10.1007/978-1-4842-3564-5 |
| Source of number or code |
doi |
| 035 ## - SYSTEM CONTROL NUMBER |
|---|
| System control number |
(DE-He213)978-1-4842-3564-5 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
DLC |
| Language of cataloging |
eng |
| Description conventions |
pn |
| -- |
rda |
| Transcribing agency |
DLC |
| 072 #7 - SUBJECT CATEGORY CODE |
|---|
| Subject category code |
COM004000 |
| Source |
bisacsh |
| 072 #7 - SUBJECT CATEGORY CODE |
|---|
| Subject category code |
UYQ |
| Source |
bicssc |
| 072 #7 - SUBJECT CATEGORY CODE |
|---|
| Subject category code |
UYQ |
| Source |
thema |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.31 |
| Edition number |
1 |
| Item number |
AYYV |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Ayyadevara, V Kishore. |
| Relator term |
author. |
| 9 (RLIN) |
31505 |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Pro machine learning algorithms : |
| Remainder of title |
a hands-on approach to implementing algorithms in python and R / |
| Statement of responsibility, etc. |
By V Kishore Ayyadevara. |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
1st ed. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
New York : |
| Name of publisher, distributor, etc. |
Apress , |
| Date of publication, distribution, etc. |
2020. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xxi,372p. ; |
| Dimensions |
23 cm. |
| 336 ## - CONTENT TYPE |
|---|
| Content type term |
text |
| Content type code |
txt |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
|---|
| Media type term |
computer |
| Media type code |
c |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
|---|
| Carrier type term |
online resource |
| Carrier type code |
cr |
| Source |
rdacarrier |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| File type |
text file |
| Encoding format |
PDF |
| Source |
rda |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Chapter 1: Basics of Machine Learning -- Chapter 2: Linear regression -- Chapter 3: Logistic regression -- Chapter 4: Decision tree -- Chapter 5: Random forest -- Chapter 6: GBM -- Chapter 7: Neural network -- Chapter 8: word2vec -- Chapter 9: Convolutional neural network -- Chapter 10: Recurrent Neural Network -- Chapter 11: Clustering -- Chapter 12: PCA -- Chapter 13: Recommender systems -- Chapter 14: Implementing algorithms in the cloud. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop the algorithm in Excel so that you get a practical understanding of all the levers that can be tuned in a model, before implementing the models in Python/R. You will cover all the major algorithms: supervised and unsupervised learning, which include linear/logistic regression; k-means clustering; PCA; recommender system; decision tree; random forest; GBM; and neural networks. You will also be exposed to the latest in deep learning through CNNs, RNNs, and word2vec for text mining. You will be learning not only the algorithms, but also the concepts of feature engineering to maximize the performance of a model. You will see the theory along with case studies, such as sentiment classification, fraud detection, recommender systems, and image recognition, so that you get the best of both theory and practice for the vast majority of the machine learning algorithms used in industry. Along with learning the algorithms, you will also be exposed to running machine-learning models on all the major cloud service providers. You are expected to have minimal knowledge of statistics/software programming and by the end of this book you should be able to work on a machine learning project with confidence. You will: Get an in-depth understanding of all the major machine learning and deep learning algorithms Fully appreciate the pitfalls to avoid while building models Implement machine learning algorithms in the cloud Follow a hands-on approach through case studies for each algorithm Gain the tricks of ensemble learning to build more accurate models Discover the basics of programming in R/Python and the Keras framework for deep learning. |
| 588 ## - SOURCE OF DESCRIPTION NOTE |
|---|
| Source of description note |
Description based on publisher-supplied MARC data. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Artificial intelligence. |
| 9 (RLIN) |
31506 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Big data. |
| 9 (RLIN) |
31507 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Computer programming. |
| 9 (RLIN) |
31508 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Open source software. |
| 9 (RLIN) |
31509 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Python (Computer program language). |
| 9 (RLIN) |
31510 |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Artificial Intelligence. |
| Authority record control number or standard number |
https://scigraph.springernature.com/ontologies/product-market-codes/I21000 |
| 9 (RLIN) |
31511 |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Big Data. |
| Authority record control number or standard number |
https://scigraph.springernature.com/ontologies/product-market-codes/I29120 |
| 9 (RLIN) |
31512 |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Open Source. |
| Authority record control number or standard number |
https://scigraph.springernature.com/ontologies/product-market-codes/I29090 |
| 9 (RLIN) |
31513 |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Python. |
| Authority record control number or standard number |
https://scigraph.springernature.com/ontologies/product-market-codes/I29080 |
| 9 (RLIN) |
31514 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Relationship information |
Print version: |
| Title |
Pro machine learning algorithms : a hands-on approach to implementing algorithms in python and r |
| International Standard Book Number |
9781484235638 |
| Record control number |
(DLC) 2018947188 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Relationship information |
Printed edition: |
| International Standard Book Number |
9781484235638 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Relationship information |
Printed edition: |
| International Standard Book Number |
9781484235652 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Relationship information |
Printed edition: |
| International Standard Book Number |
9781484245651 |
| 906 ## - LOCAL DATA ELEMENT F, LDF (RLIN) |
|---|
| a |
0 |
| b |
ibc |
| c |
origres |
| d |
u |
| e |
ncip |
| f |
20 |
| g |
y-gencatlg |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Book |
| Edition |
1st |
| Call number prefix |
006.31 AYYV |
