Distributed Machine Learning with PySpark

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.

Abdelaziz Testas, Ph.D., is a data scientist with over a decade of experience in data analysis and machine learning, specializing in the use of standard Python libraries and Spark distributed computing. He holds a Ph.D. in Economics from Leeds University and a Master's degree in Finance from Glasgow University. He has also earned several certificates in computer science and data science.

In the last ten years, he has worked for Nielsen in Fremont, California as a Lead Data Scientist focused on improving the company’s audience measurement through planning, initiating, and executing end-to-end data science projects and methodology work. He has created advanced solutions for Nielsen’s digital ad and content rating products by leveraging subject matter expertise in media measurement and data science. He is passionate about helping others improve their machine learning skills and workflows, and is excited to share his knowledge and experience with a wider audience through this book.