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Machine Learning with PySpark

With Natural Language Processing and Recommender Systems

  • Book
  • © 2019

Overview

Authors:
  1. Pramod Singh

    1. Bangalore, India

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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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Table of contents (9 chapters)

  1. Front Matter

    Pages i-xviii
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  2. Evolution of Data

    • Pramod Singh
    Pages 1-10

  3. Introduction to Machine Learning

    • Pramod Singh
    Pages 11-21

  4. Data Processing

    • Pramod Singh
    Pages 23-42

  5. Linear Regression

    • Pramod Singh
    Pages 43-64

  6. Logistic Regression

    • Pramod Singh
    Pages 65-98

  7. Random Forests

    • Pramod Singh
    Pages 99-122

  8. Recommender Systems

    • Pramod Singh
    Pages 123-157

  9. Clustering

    • Pramod Singh
    Pages 159-190

  10. Natural Language Processing

    • Pramod Singh
    Pages 191-218

  11. Back Matter

    Pages 219-223
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Keywords

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 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

  • Bangalore, India

    Pramod Singh

About the author

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.

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