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001443334 001__ 1443334
001443334 003__ OCoLC
001443334 005__ 20230310003539.0
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001443334 019__ $$a1289370197
001443334 020__ $$a9781484277775$$q(electronic bk.)
001443334 020__ $$a1484277775$$q(electronic bk.)
001443334 020__ $$z9781484277768
001443334 020__ $$z1484277767
001443334 0247_ $$a10.1007/978-1-4842-7777-5$$2doi
001443334 035__ $$aSP(OCoLC)1290020510
001443334 040__ $$aEBLCP$$beng$$erda$$cEBLCP$$dTOH$$dORMDA$$dOCLCO$$dOCLCF$$dYDX$$dGW5XE$$dOCLCO$$dOCLCQ
001443334 049__ $$aISEA
001443334 050_4 $$aQA76.76.A65$$bS55 2022
001443334 08204 $$a005.7$$223
001443334 1001_ $$aSingh, Pramod,$$eauthor.
001443334 24510 $$aMachine learning with Pyspark :$$bwith natural language processing and recommender systems /$$cPramod Singh.
001443334 250__ $$aSecond edition.
001443334 264_1 $$aCalifornia:$$bApress,$$c[2022]
001443334 300__ $$a1 online resource
001443334 336__ $$atext$$btxt$$2rdacontent
001443334 337__ $$acomputer$$bc$$2rdamedia
001443334 338__ $$aonline resource$$bcr$$2rdacarrier
001443334 500__ $$aIncludes index.
001443334 5050_ $$aChapter 1: Introduction to Spark 3.1 -- Chapter 2: Manage Data with PySpark -- Chapter 3: Introduction to Machine Learning -- Chapter 4: Linear Regression with PySpark -- Chapter 5: Logistic Regression with PySpark -- Chapter 6: Ensembling with PySpark -- Chapter 7: Clustering with PySpark -- Chapter 8: Recommendation Engine with PySpark -- Chapter 9: Advanced Feature Engineering with PySpark.
001443334 506__ $$aAccess limited to authorized users.
001443334 520__ $$aMaster the new features in PySpark 3.1 to develop data-driven, intelligent applications. This updated edition covers topics ranging from building scalable machine learning models, to natural language processing, to recommender systems. Machine Learning with PySpark, Second Edition begins with the fundamentals of Apache Spark, including the latest updates to the framework. Next, you will learn the full spectrum of traditional machine learning algorithm implementations, along with natural language processing and recommender systems. You'll gain familiarity with the critical process of selecting machine learning algorithms, data ingestion, and data processing to solve business problems. You'll see a demonstration of how to build supervised machine learning models such as linear regression, logistic regression, decision trees, and random forests. You'll also learn how to automate the steps using Spark pipelines, followed by unsupervised models such as K-means and hierarchical clustering. A section on Natural Language Processing (NLP) covers text processing, text mining, and embeddings for classification. This new edition also introduces Koalas in Spark and how to automate data workflow using Airflow and PySpark's latest ML library. After completing this book, you will understand how to use PySpark's machine learning library to build and train various machine learning models, along with related components such as data ingestion, processing and visualization to develop data-driven intelligent applications What you will learn: Build a spectrum of supervised and unsupervised machine learning  algorithms Use PySpark's machine learning library to implement machine learning and recommender systems Leverage the new features in PySpark's machine learning library Understand data processing using Koalas in Spark Handle issues around feature engineering, class balance, bias and variance, and cross validation to build optimally fit models Who This Book Is For Data science and machine learning professionals.
001443334 588__ $$aDescription based on online resource; title from digital title page (viewed on February 15, 2022).
001443334 650_0 $$aApplication software$$xDevelopment.
001443334 650_0 $$aPython (Computer program language)
001443334 650_0 $$aSPARK (Computer program language)
001443334 650_6 $$aLogiciels d'application$$xDéveloppement.
001443334 650_6 $$aPython (Langage de programmation)
001443334 655_0 $$aElectronic books.
001443334 77608 $$iPrint version:$$aSingh, Pramod$$tMachine Learning with Pyspark$$dBerkeley, CA : Apress L. P.,c2021$$z9781484277768
001443334 852__ $$bebk
001443334 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-1-4842-7777-5$$zOnline Access$$91397441.1
001443334 909CO $$ooai:library.usi.edu:1443334$$pGLOBAL_SET
001443334 980__ $$aBIB
001443334 980__ $$aEBOOK
001443334 982__ $$aEbook
001443334 983__ $$aOnline
001443334 994__ $$a92$$bISE