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Machine Learning with PySpark

With Natural Language Processing and Recommender Systems

Authors: Singh, Pramod

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  • Covers all PySpark machine learning models including PySpark advanced methods
  • Contains practical applications of machine learning algorithms
  • Presents advanced features of engineering techniques for machine learning models
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eBook ¥2,807
price for Japan (gross)
  • ISBN 978-1-4842-4131-8
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover ¥3,509
price for Japan (gross)
  • ISBN 978-1-4842-4130-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

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.
What You Will Learn

  • 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

Who This Book Is For 
Data science and machine learning professionals. 

About the authors

Pramod Singh is an established data scientist with over eight years of experience in data and solving business challenges. He has worked in organizations such as Infosys, Tally and SapientRazorfish. Also, president of a data science meet-up group and regular speaker at various webinars. Recently spoke at major conference: GIDS 2018 and presented a session on “Sequence Embedding in Spark” which was well received. He has an online Udemy course on machine learning.

Table of contents (9 chapters)

Table of contents (9 chapters)

Buy this book

eBook ¥2,807
price for Japan (gross)
  • ISBN 978-1-4842-4131-8
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover ¥3,509
price for Japan (gross)
  • ISBN 978-1-4842-4130-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.

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Bibliographic Information

Bibliographic Information
Book Title
Machine Learning with PySpark
Book Subtitle
With Natural Language Processing and Recommender Systems
Authors
Copyright
2019
Publisher
Apress
Copyright Holder
Pramod Singh
eBook ISBN
978-1-4842-4131-8
DOI
10.1007/978-1-4842-4131-8
Softcover ISBN
978-1-4842-4130-1
Edition Number
1
Number of Pages
XVIII, 223
Number of Illustrations
149 b/w illustrations, 1 illustrations in colour
Topics