MARC details
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OCoLC |
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20240829150601.0 |
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190525s2019 enk o 000 0 eng d |
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EBLCP |
| Language of cataloging |
eng |
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rda |
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pn |
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EBLCP |
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HNK |
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EBLCP |
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N$T |
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TEFOD |
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OCLCF |
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YDX |
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UKAHL |
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OCLCQ |
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NLW |
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OCLCO |
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OCLCQ |
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OCLCO |
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NZAUC |
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OCLCQ |
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OCLCO |
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OCLCL |
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TMA |
| 019 ## - |
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1102475541 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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1789347505 |
| Qualifying information |
(ebook) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781789347500 |
| Qualifying information |
(electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| Canceled/invalid ISBN |
9781789349795 |
| 035 ## - SYSTEM CONTROL NUMBER |
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2142587 |
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(N$T) |
| 035 ## - SYSTEM CONTROL NUMBER |
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| System control number |
(OCoLC)1102472595 |
| Canceled/invalid control number |
(OCoLC)1102475541 |
| 037 ## - SOURCE OF ACQUISITION |
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| Stock number |
603AF57C-58D1-4353-9D21-9D1D72B0AC05 |
| Source of stock number/acquisition |
OverDrive, Inc. |
| Note |
http://www.overdrive.com |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
Q325.5 |
| Item number |
.M46 2019eb |
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COM |
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000000 |
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bisacsh |
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006.31 |
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23 |
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MAIN |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Mengle, Saket S. R., |
| Relator term |
author. |
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| 245 10 - TITLE STATEMENT |
|---|
| Title |
Mastering machine learning on AWS : |
| Remainder of title |
advanced machine learning in Python using SageMaker, Apache Spark, and TensorFlow / |
| Statement of responsibility, etc. |
Saket S.R. Mengle, Maximo Gurmendez. |
| 246 3# - VARYING FORM OF TITLE |
|---|
| Title proper/short title |
Mastering machine learning on Amazon Web Services |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Birmingham, UK : |
| Name of producer, publisher, distributor, manufacturer |
Packt Publishing, Limited, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2019. |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
1 online resource (293 pages) |
| 336 ## - CONTENT TYPE |
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text |
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txt |
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rdacontent |
| 337 ## - MEDIA TYPE |
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computer |
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c |
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rdamedia |
| 338 ## - CARRIER TYPE |
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| Carrier type term |
online resource |
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cr |
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rdacarrier |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Cover; Title Page; Copyright and Credits; Dedication; About Packt; Contributors; Table of Contents; Preface; Section 1: Machine Learning on AWS; Chapter 1: Getting Started with Machine Learning for AWS; How AWS empowers data scientists; Using AWS tools for machine learning; Identifying candidate problems that can be solved using machine learning; Machine learning project life cycle; Data gathering; Evaluation metrics; Algorithm selection; Deploying models; Summary; Exercise; Section 2: Implementing Machine Learning Algorithms at Scale on AWS |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Chapter 2: Classifying Twitter Feeds with Naive BayesClassification algorithms; Feature types; Nominal features; Ordinal features; Continuous features; Naive Bayes classifier; Bayes' theorem; Posterior; Likelihood; Prior probability; Evidence; How the Naive Bayes algorithm works; Classifying text with language models; Collecting the tweets; Preparing the data; Building a Naive Bayes model through SageMaker notebooks; Naïve Bayes model on SageMaker notebooks using Apache Spark; Using SageMaker's BlazingText built-in ML service; Naive Bayes -- pros and cons; Summary; Exercises |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Chapter 3: Predicting House Value with Regression AlgorithmsPredicting the price of houses; Understanding linear regression; Linear least squares estimation; Maximum likelihood estimation; Gradient descent; Evaluating regression models; Mean absolute error; Mean squared error; Root mean squared error; R-squared; Implementing linear regression through scikit-learn; Implementing linear regression through Apache Spark; Implementing linear regression through SageMaker's linear Learner; Understanding logistic regression; Logistic regression in Spark; Pros and cons of linear models; Summary |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Chapter 4: Predicting User Behavior with Tree-Based MethodsUnderstanding decision trees; Recursive splitting; Types of decision trees; Cost functions; Gini Impurity; Information gain; Criteria to stop splitting trees; Understanding random forest algorithms; Understanding gradient boosting algorithms; Predicting clicks on log streams; Introduction to Elastic Map Reduce (EMR); Training with Apache Spark on EMR; Getting the data; Preparing the data; Categorical encoding; One-hot encoding; Training a model; Evaluating our model; Area Under ROC Curve; Area under the precision-recall curve; Training tree ensembles on EMR Training gradient-boosted trees with the SageMaker services; Preparing the data; Training with SageMaker XGBoost; Applying and evaluating the model; Summary; Exercises |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Chapter 5: Customer Segmentation Using Clustering Algorithms; Understanding How Clustering Algorithms Work; k-means clustering; Euclidean distance; Manhattan distance; Hierarchical clustering; Agglomerative clustering; Divisive clustering; Clustering with Apache Spark on EMR; Clustering with Spark and SageMaker on EMR; Understanding the purpose of the IAM role; Summary; Exercises; Chapter 6: Analyzing Visitor Patterns to Make Recommendations |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
This book will help you master your skills in various artificial intelligence and machine learning services available on AWS. Through practical hands-on examples, you'll learn how to use these services to generate impressive results. You will have a tremendous understanding of how to use a wide range of AWS services in your own organization. |
| 588 0# - SOURCE OF DESCRIPTION NOTE |
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Print version record. |
| 590 ## - LOCAL NOTE (RLIN) |
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| Local note |
Added to collection customer.56279.3 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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Machine learning. |
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1680 |
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Python (Computer program language) |
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985 |
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Data mining. |
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Data Mining |
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Machine Learning |
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1680 |
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Apprentissage automatique. |
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18971 |
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Python (Langage de programmation) |
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Artificial intelligence. |
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bisacsh |
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Gurmendez, Maximo, |
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author. |
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| 758 ## - |
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has work: |
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Mastering machine learning on AWS (Text) |
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https://id.oclc.org/worldcat/entity/E39PCYP7tfP8WXKc6C7Hwj49wy |
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https://id.oclc.org/worldcat/ontology/hasWork |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
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| Relationship information |
Print version: |
| Main entry heading |
Mengle, Saket S.R. |
| Title |
Mastering machine learning on AWS : advanced machine learning in Python Using SageMaker, Apache Spark, and TensorFlow. |
| Place, publisher, and date of publication |
Birmingham : Packt Publishing, Limited, ©2019 |
| International Standard Book Number |
9781789349795 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
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EBSCOhost |
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<a href="https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2142587">https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2142587</a> |
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Askews and Holts Library Services |
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ASKH |
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AH36312440 |
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ProQuest Ebook Central |
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EBLB |
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EBL5778831 |
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EBSCOhost |
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EBSC |
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2142587 |
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YBP Library Services |
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YANK |
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300558441 |
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