Apache Spark 2.x Machine Learning Cookbook:
bSimplify machine learning model implementations with Spark/bh2About This Book/h2ulliSolve the day-to-day problems of data science with Spark/liliThis unique cookbook consists of exciting and intuitive numerical recipes/liliOptimize your work by acquiring, cleaning, analyzing, predicting, and visual...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham
Packt Publishing Limited
2017
|
| Ausgabe: | 1 |
| Schlagworte: | |
| Zusammenfassung: | bSimplify machine learning model implementations with Spark/bh2About This Book/h2ulliSolve the day-to-day problems of data science with Spark/liliThis unique cookbook consists of exciting and intuitive numerical recipes/liliOptimize your work by acquiring, cleaning, analyzing, predicting, and visualizing your data/li/ulh2Who This Book Is For/h2This 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.h2What You Will Learn/h2ulliGet to know how Scala and Spark go hand-in-hand for developers when developing ML systems with Spark/liliBuild a recommendation engine that scales with Spark/liliFind out how to build unsupervised clustering systems to classify data in Spark/liliBuild machine learning systems with the Decision Tree and Ensemble models in Spark/liliDeal with the curse of high-dimensionality in big data using Spark/liliImplement Text analytics for Search Engines in Spark/liliStreaming Machine Learning System implementation using Spark/li/ulh2In Detail/h2Machine 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. |
| Beschreibung: | 1 Online-Ressource (666 Seiten) |
| ISBN: | 9781782174608 |
Internformat
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| 520 | |a bSimplify machine learning model implementations with Spark/bh2About This Book/h2ulliSolve the day-to-day problems of data science with Spark/liliThis unique cookbook consists of exciting and intuitive numerical recipes/liliOptimize your work by acquiring, cleaning, analyzing, predicting, and visualizing your data/li/ulh2Who This Book Is For/h2This 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. | ||
| 520 | |a However, you do not need to be acquainted with the Spark ML libraries and ecosystem.h2What You Will Learn/h2ulliGet to know how Scala and Spark go hand-in-hand for developers when developing ML systems with Spark/liliBuild a recommendation engine that scales with Spark/liliFind out how to build unsupervised clustering systems to classify data in Spark/liliBuild machine learning systems with the Decision Tree and Ensemble models in Spark/liliDeal with the curse of high-dimensionality in big data using Spark/liliImplement Text analytics for Search Engines in Spark/liliStreaming Machine Learning System implementation using Spark/li/ulh2In Detail/h2Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability, and optimization. | ||
| 520 | |a 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. | ||
| 650 | 4 | |a COMPUTERS / Intelligence (AI) & | |
| 650 | 4 | |a Semantics | |
| 650 | 4 | |a COMPUTERS / Machine Theory | |
| 700 | 1 | |a Rajendran, Meenakshi |e Sonstige |4 oth | |
| 700 | 1 | |a Hall, Broderick |e Sonstige |4 oth | |
| 700 | 1 | |a Mei, Shuen |e Sonstige |4 oth | |
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| indexdate | 2024-07-10T09:01:44Z |
| institution | BVB |
| isbn | 9781782174608 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032476857 |
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| spelling | Amirghodsi, Siamak Verfasser aut Apache Spark 2.x Machine Learning Cookbook Amirghodsi, Siamak 1 Birmingham Packt Publishing Limited 2017 1 Online-Ressource (666 Seiten) txt rdacontent c rdamedia cr rdacarrier bSimplify machine learning model implementations with Spark/bh2About This Book/h2ulliSolve the day-to-day problems of data science with Spark/liliThis unique cookbook consists of exciting and intuitive numerical recipes/liliOptimize your work by acquiring, cleaning, analyzing, predicting, and visualizing your data/li/ulh2Who This Book Is For/h2This 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.h2What You Will Learn/h2ulliGet to know how Scala and Spark go hand-in-hand for developers when developing ML systems with Spark/liliBuild a recommendation engine that scales with Spark/liliFind out how to build unsupervised clustering systems to classify data in Spark/liliBuild machine learning systems with the Decision Tree and Ensemble models in Spark/liliDeal with the curse of high-dimensionality in big data using Spark/liliImplement Text analytics for Search Engines in Spark/liliStreaming Machine Learning System implementation using Spark/li/ulh2In Detail/h2Machine 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. COMPUTERS / Intelligence (AI) & Semantics COMPUTERS / Machine Theory Rajendran, Meenakshi Sonstige oth Hall, Broderick Sonstige oth Mei, Shuen Sonstige oth |
| spellingShingle | Amirghodsi, Siamak Apache Spark 2.x Machine Learning Cookbook COMPUTERS / Intelligence (AI) & Semantics COMPUTERS / Machine Theory |
| title | Apache Spark 2.x Machine Learning Cookbook |
| title_auth | Apache Spark 2.x Machine Learning Cookbook |
| title_exact_search | Apache Spark 2.x Machine Learning Cookbook |
| title_exact_search_txtP | Apache Spark 2.x Machine Learning Cookbook |
| title_full | Apache Spark 2.x Machine Learning Cookbook Amirghodsi, Siamak |
| title_fullStr | Apache Spark 2.x Machine Learning Cookbook Amirghodsi, Siamak |
| title_full_unstemmed | Apache Spark 2.x Machine Learning Cookbook Amirghodsi, Siamak |
| title_short | Apache Spark 2.x Machine Learning Cookbook |
| title_sort | apache spark 2 x machine learning cookbook |
| topic | COMPUTERS / Intelligence (AI) & Semantics COMPUTERS / Machine Theory |
| topic_facet | COMPUTERS / Intelligence (AI) & Semantics COMPUTERS / Machine Theory |
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