Apache Spark 2.x Machine Learning Cookbook (notice n° 70019)
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| fixed length control field | 03666cam a2200301zu 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | FRCYB88855143 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250107225825.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250107s2017 fr | o|||||0|0|||eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781783551606 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | FRCYB88855143 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | FR-PaCSA |
| Language of cataloging | en |
| Transcribing agency | |
| Description conventions | rda |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Amirghodsi, Siamak |
| 245 01 - TITLE STATEMENT | |
| Title | Apache Spark 2.x Machine Learning Cookbook |
| Statement of responsibility, etc. | ['Amirghodsi, Siamak', 'Rajendran, Meenakshi', 'Hall, Broderick'] |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Name of producer, publisher, distributor, manufacturer | Packt Publishing |
| Date of production, publication, distribution, manufacture, or copyright notice | 2017 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | p. |
| 336 ## - CONTENT TYPE | |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type code | c |
| Source | rdamdedia |
| 338 ## - CARRIER TYPE | |
| Carrier type code | c |
| Source | rdacarrier |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Simplify machine learning model implementations with SparkAbout This BookSolve the day-to-day problems of data science with SparkThis unique cookbook consists of exciting and intuitive numerical recipesOptimize your work by acquiring, cleaning, analyzing, predicting, and visualizing your dataWho This Book Is ForThis book is for Scala developers with a fairly good exposure to and understanding of machine learning techniques, but lack practical implementations with Spark. A solid knowledge of machine learning algorithms is assumed, as well as hands-on experience of implementing ML algorithms with Scala. However, you do not need to be acquainted with the Spark ML libraries and ecosystem.What You Will LearnGet to know how Scala and Spark go hand-in-hand for developers when developing ML systems with SparkBuild a recommendation engine that scales with SparkFind out how to build unsupervised clustering systems to classify data in SparkBuild machine learning systems with the Decision Tree and Ensemble models in SparkDeal with the curse of high-dimensionality in big data using SparkImplement Text analytics for Search Engines in SparkStreaming Machine Learning System implementation using SparkIn DetailMachine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability, and optimization. Learning about algorithms enables a wide range of applications, from everyday tasks such as product recommendations and spam filtering to cutting edge applications such as self-driving cars and personalized medicine. You will gain hands-on experience of applying these principles using Apache Spark, a resilient cluster computing system well suited for large-scale machine learning tasks.This book begins with a quick overview of setting up the necessary IDEs to facilitate the execution of code examples that will be covered in various chapters. It also highlights some key issues developers face while working with machine learning algorithms on the Spark platform. We progress by uncovering the various Spark APIs and the implementation of ML algorithms with developing classification systems, recommendation engines, text analytics, clustering, and learning systems. Toward the final chapters, we'll focus on building high-end applications and explain various unsupervised methodologies and challenges to tackle when implementing with big data ML systems.Style and approachThis book is packed with intuitive recipes supported with line-by-line explanations to help you understand how to optimize your work flow and resolve problems when working with complex data modeling tasks and predictive algorithms. This is a valuable resource for data scientists and those working on large scale data projects. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Amirghodsi, Siamak |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Rajendran, Meenakshi |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Hall, Broderick |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Access method | Cyberlibris |
| Uniform Resource Identifier | <a href="https://international.scholarvox.com/netsen/book/88855143">https://international.scholarvox.com/netsen/book/88855143</a> |
| Electronic format type | text/html |
| Host name | |
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