東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ subject:"SPARK (Computer program language)" ]

切換: 標籤 | MARC模式 | ISBD

Machine learning with Pyspark = with...

Singh, Pramod.

FindBook

Google Book

Amazon

博客來

Machine learning with Pyspark = with natural language processing and recommender systems /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning with Pyspark/ by Pramod Singh.
其他題名:	with natural language processing and recommender systems /
作者:	Singh, Pramod.
出版者:	Berkeley, CA :Apress : : 2022.,
面頁冊數:	xviii, 220 p. :ill., digital ;24 cm.
內容註:	Chapter 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.
Contained By:	Springer Nature eBook
標題:	Application software - Development. -
電子資源:	https://doi.org/10.1007/978-1-4842-7777-5
ISBN:	9781484277775

Machine learning with Pyspark = with natural language processing and recommender systems /
Singh, Pramod.

Machine learning with Pysparkwith natural language processing and recommender systems /[electronic resource] :by Pramod Singh. - Second edition. - Berkeley, CA :Apress :2022. - xviii, 220 p. :ill., digital ;24 cm.

Chapter 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.

Master 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 You will: 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.

ISBN: 9781484277775

Standard No.: 10.1007/978-1-4842-7777-5doiSubjects--Topical Terms:

539563
Application software
--Development.

LC Class. No.: QA76.76.A65 / S55 2022

Dewey Class. No.: 005.7

Machine learning with Pyspark = with natural language processing and recommender systems /
LDR:03413nmm a2200337 a 4500 001 2297864
003 DE-He213
005 20220124160549.0
006 m d
007 cr nn 008maaau
008 230324s2022 cau s 0 eng d
020 $a 9781484277775 $q (electronic bk.)
020 $a 9781484277768 $q (paper)
024 7 $a 10.1007/978-1-4842-7777-5 $2 doi
035 $a 978-1-4842-7777-5
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.76.A65 $b S55 2022
072 7 $a UYQM $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 005.7 $2 23
090 $a QA76.76.A65 $b S617 2022
100 1 $a Singh, Pramod. $3 3384003
245 1 0 $a Machine learning with Pyspark $h [electronic resource] : $b with natural language processing and recommender systems / $c by Pramod Singh.
250 $a Second edition.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2022.
300 $a xviii, 220 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 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.
520 $a Master 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 You will: 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.
650 0 $a Application software $x Development. $3 539563
650 0 $a Python (Computer program language) $3 729789
650 0 $a SPARK (Computer program language) $3 3300294
650 1 4 $a Machine Learning. $3 3382522
650 2 4 $a Python. $3 3201289
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Open Source. $3 2210577
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-1-4842-7777-5
950 $a Professional and Applied Computing (SpringerNature-12059)