Machine Learning with PySpark

1. Front Matter

   Pages i-xviii

   PDF

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

    PDF

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. 

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

• Bangalore, India

  Pramod Singh

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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