Distributed Machine Learning with PySpark

Migrating Effortlessly from Pandas and Scikit-Learn

Abdelaziz Testas

• Publisher: Apress
• Year: 2023
• ISBN:
  9781484297513
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

Migrate from pandas and scikit-learn to PySpark to handle vast amounts of data and achieve faster data processing time. This book will show you how to make this transition by adapting your skills and leveraging the similarities in syntax, functionality, and interoperability between these tools.

Distributed Machine Learning with PySpark offers a roadmap to data scientists considering transitioning from small data libraries (pandas/scikit-learn) to big data processing and machine learning with PySpark. You will learn to translate Python code from pandas/scikit-learn to PySpark to preprocess large volumes of data and build, train, test, and evaluate popular machine learning algorithms such as linear and logistic regression, decision trees, random forests, support vector machines, Naïve Bayes, and neural networks.

After completing this book, you will understand the foundational concepts of data preparation and machine learning and will have the skills necessary toapply these methods using PySpark, the industry standard for building scalable ML data pipelines.

What You Will Learn

• Master the fundamentals of supervised learning, unsupervised learning, NLP, and recommender systems
• Understand the differences between PySpark, scikit-learn, and pandas
• Perform linear regression, logistic regression, and decision tree regression with pandas, scikit-learn, and PySpark
• Distinguish between the pipelines of PySpark and scikit-learn

Who This Book Is For

Data scientists, data engineers, and machine learning practitioners who have some familiarity with Python, but who are new to distributed machine learning and the PySpark framework.

Table of contents

• Cover
• Front Matter
• 1. An Easy Transition
• 2. Selecting Algorithms
• 3. Multiple Linear Regression with Pandas, Scikit-Learn, and PySpark
• 4. Decision Tree Regression with Pandas, Scikit-Learn, and PySpark
• 5. Random Forest Regression with Pandas, Scikit-Learn, and PySpark
• 6. Gradient-Boosted Tree Regression with Pandas, Scikit-Learn, and PySpark
• 7. Logistic Regression with Pandas, Scikit-Learn, and PySpark
• 8. Decision Tree Classification with Pandas, Scikit-Learn, and PySpark
• 9. Random Forest Classification with Scikit-Learn and PySpark
• 10. Support Vector Machine Classification with Pandas, Scikit-Learn, and PySpark
• 11. Naive Bayes Classification with Pandas, Scikit-Learn, and PySpark
• 12. Neural Network Classification with Pandas, Scikit-Learn, and PySpark
• 13. Recommender Systems with Pandas, Surprise, and PySpark
• 14. Natural Language Processing with Pandas, Scikit-Learn, and PySpark
• 15. k-Means Clustering with Pandas, Scikit-Learn, and PySpark
• 16. Hyperparameter Tuning with Scikit-Learn and PySpark
• 17. Pipelines with Scikit-Learn and PySpark
• 18. Deploying Models in Production with Scikit-Learn and PySpark
• Back Matter