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Machine learning with PySparkwith na...

Singh, Pramod.

Machine learning with PySparkwith 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 PySparkby Pramod Singh.
其他題名:	with natural language processing and recommender systems /
作者:	Singh, Pramod.
出版者:	Berkeley, CA :Apress :2019.
面頁冊數:	xviii, 223 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Application softwareDevelopment.
電子資源:	https://doi.org/10.1007/978-1-4842-4131-8
ISBN:	9781484241318$q(electronic bk.)

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

Machine learning with PySparkwith natural language processing and recommender systems /[electronic resource] :by Pramod Singh. - Berkeley, CA :Apress :2019. - xviii, 223 p. :ill., digital ;24 cm.

Build machine learning models, natural language processing applications, and recommender systems with PySpark to solve various business challenges. This book starts with the fundamentals of Spark and its evolution and then covers the entire spectrum of traditional machine learning algorithms along with natural language processing and recommender systems using PySpark. Machine Learning with PySpark shows you how to build supervised machine learning models such as linear regression, logistic regression, decision trees, and random forest. You'll also see unsupervised machine learning models such as K-means and hierarchical clustering. A major portion of the book focuses on feature engineering to create useful features with PySpark to train the machine learning models. The natural language processing section covers text processing, text mining, and embedding for classification. After reading this book, you will understand how to use PySpark's machine learning library to build and train various machine learning models. Additionally you'll become comfortable with related PySpark components, such as data ingestion, data processing, and data analysis, that you can use to develop data-driven intelligent applications. You will: Build a spectrum of supervised and unsupervised machine learning algorithms Implement machine learning algorithms with Spark MLlib libraries Develop a recommender system with Spark MLlib libraries Handle issues related to feature engineering, class balance, bias and variance, and cross validation for building an optimal fit model.

ISBN: 9781484241318$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-4131-8doiSubjects--Topical Terms:

189413
Application software
--Development.

LC Class. No.: QA76.76.A65

Dewey Class. No.: 005.7

Machine learning with PySparkwith natural language processing and recommender systems /
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