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Machine learning with Pyspark = with...

Singh, Pramod.

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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning with Pyspark/ by Pramod Singh.
Reminder of title:	with natural language processing and recommender systems /
Author:	Singh, Pramod.
Published:	Berkeley, CA :Apress : : 2022.,
Description:	xviii, 220 p. :ill., digital ;24 cm.
[NT 15003449]:	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
Subject:	Application software - Development. -
Online resource:	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 /
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245 1 0 $a Machine learning with Pyspark $h [electronic resource] : $b with natural language processing and recommender systems / $c by Pramod Singh.
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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
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773 0 $t Springer Nature eBook
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950 $a Professional and Applied Computing (SpringerNature-12059)