Overview
- 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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Table of contents (9 chapters)
Keywords
About this book
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 thisbook, 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.
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Machine Learning with PySpark
Book Subtitle: With Natural Language Processing and Recommender Systems
Authors: Pramod Singh
DOI: https://doi.org/10.1007/978-1-4842-4131-8
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Pramod Singh 2019
eBook ISBN: 978-1-4842-4131-8Published: 14 December 2018
Edition Number: 1
Number of Pages: XVIII, 223
Number of Illustrations: 149 b/w illustrations, 1 illustrations in colour
Topics: Artificial Intelligence, Python, Big Data/Analytics, Open Source