Machine Learning with PySpark

With Natural Language Processing and Recommender Systems

  • Pramod Singh

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Pramod Singh
    Pages 1-10
  3. Pramod Singh
    Pages 11-21
  4. Pramod Singh
    Pages 23-42
  5. Pramod Singh
    Pages 43-64
  6. Pramod Singh
    Pages 65-98
  7. Pramod Singh
    Pages 99-122
  8. Pramod Singh
    Pages 123-157
  9. Pramod Singh
    Pages 159-190
  10. Pramod Singh
    Pages 191-218
  11. Back Matter
    Pages 219-223

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.

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


Machine Learning PySpark Python Supervised Learning Unsurpervised Learning Reinforcement Learning Recommender Systems

Authors and affiliations

  • Pramod Singh
    • 1
  1. 1.BangaloreIndia

Bibliographic information

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