MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02305cam a22002897a 4500 |
| CONTROL NUMBER |
|---|
| control field |
20673517 |
| DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20190411104427.0 |
| FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
180918t20182018caua 001 0 eng d |
| LIBRARY OF CONGRESS CONTROL NUMBER |
|---|
| LC control number |
2018303264 |
| INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9789352137251 |
| SYSTEM CONTROL NUMBER |
|---|
| System control number |
(OCoLC)on1031406477 |
| CATALOGING SOURCE |
|---|
| Original cataloging agency |
SXP |
| Language of cataloging |
eng |
| Transcribing agency |
SXP |
| Modifying agency |
SXP |
| -- |
JRZ |
| -- |
OQX |
| -- |
EYM |
| -- |
OCLCF |
| -- |
DLC |
| AUTHENTICATION CODE |
|---|
| Authentication code |
lccopycat |
| LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
Q325.5 |
| Item number |
.B85 2018 |
| DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.31 |
| Edition number |
23 |
| Item number |
BUR |
| MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Burger, Scott V., |
| Relator term |
author. |
| TITLE STATEMENT |
|---|
| Title |
Introduction to machine learning with R : |
| Remainder of title |
rigorous mathematical analysis / |
| Statement of responsibility, etc |
Scott V. Burger. |
| PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Mumbai : |
| Name of publisher, distributor, etc |
Shroff Publishers& Distributors, |
| Date of publication, distribution, etc |
2018. |
| PHYSICAL DESCRIPTION |
|---|
| Extent |
ix, 212 p. : |
| Other physical details |
ill. ; |
| Dimensions |
24 cm |
| GENERAL NOTE |
|---|
| General note |
Includes index. |
| FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
What is a model? -- Supervised and unsupervised machine learning -- Sampling statistics and model training in R -- Regression in a nutshell -- Neural networks in a nutshell -- Tree-based methods -- Other advanced methods -- Machine learning with the caret package -- Encyclopedia of machine learning models in caret. |
| SUMMARY, ETC. |
|---|
| Summary, etc |
Machine learning can be a difficult subject if you're not familiar with the basics. With this book, you'll get a solid foundation of introductory principles used in machine learning with the statistical programming language R. You'll start with the basics like regression, then move into more advanced topics like neural networks, and finally delve into the frontier of machine learning in the R world with packages like Caret. By developing a familiarity with topics like understanding the difference between regression and classification models, you'll be able to solve an array of machine learning problems. Knowing when to use a specific model or not can mean the difference between a highly accurate model and a completely useless one. This book provides copious examples to build a working knowledge of machine learning. Understand the major parts of machine learning algorithms Recognize how machine learning can be used to solve a problem in a simple manner Figure out when to use certain machine learning algorithms versus others Learn how to operationalize algorithms with cutting edge packages |
| SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Machine learning. |
| SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
R (Computer program language) |
| SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Statistics |
| General subdivision |
Data processing. |
| ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Item type |
Books |
