Machine Learning with PySpark: With Natural Language Processing and Recommender Systems

Author

Master the new features in PySpark 3.1 to develop data-driven, intelligent applications. This updated edition covers topics ranging from building scalable machine learning models, to natural language processing, to recommender systems.

Machine Learning with PySpark, Second Edition begins with the fundamentals of Apache Spark, including the latest updates to the framework. Next, you will learn the full spectrum of traditional machine learning algorithm implementations, along with natural language processing and recommender systems. Youll gain familiarity with the critical process of selecting machine learning algorithms, data ingestion, and data processing to solve business problems. Youll see a demonstration of how to build supervised machine learning models such as linear regression, logistic regression, decision trees, and random forests. Youll also learn how to automate the steps using Spark pipelines, followed by unsupervised models such as K-means and hierarchical clustering. A section on Natural Language Processing (NLP) covers text processing, text mining, and embeddings for classification. This new edition also introduces Koalas in Spark and how to automate data workflow using Airflow and PySparks latest ML library.

After completing this book, you will understand how to use PySparks machine learning library to build and train various machine learning models, along with related components such as data ingestion, processing and visualization to develop data-driven intelligent applications

What you will learn:

Build a spectrum of supervised and unsupervised machine learning algorithms
Use PySpark\'s machine learning library to implement machine learning and recommender systems
Leverage the new features in PySparks machine learning library
Understand data processing using Koalas in Spark
Handle issues around feature engineering, class balance, bias and variance, and cross validation to build optimally fit models

Who This Book Is For

Data science and machine learning professionals.

Download pdf

ISBN-10

1484277767

ISBN-13

978-1484277768

Publisher

Apress

Price

33.87

File Type

PDF

Page No.

240

Pramod Singh

...

From the Back Cover

You will:

Build a spectrum of supervised and unsupervised machine learning algorithms
Use PySpark's machine learning library to implement machine learning and recommender systems
Leverage the new features in PySparks machine learning library
Understand data processing using Koalas in Spark
Handle issues around feature engineering, class balance, bias and variance, and cross validation to build optimally fit models

About the Author

Pramod Singh works at Bain & Company in the Advanced Analytics Group. He has extensive hands-on experience in large scale machine learning, deep learning, data engineering, designing algorithms and application development. He has spent more than 13 years working in the field of Data and AI at different organizations. Hes published four books Deploy Machine Learning Models to Production, Machine Learning with PySpark, Learn PySpark and Learn TensorFlow 2.0, all for Apress. He is also a regular speaker at major conferences such as OReillys Strata and AI conferences. Pramod holds a BTech in electrical engineering from B.A.T.U, and an MBA from Symbiosis University. He has also earned a Data Science certification from IIMCalcutta. He lives in Gurgaon with his wife and 5-year-old son. In his spare time, he enjoys playing guitar, coding, reading, and watching football.

Machine Learning with PySpark: With Natural Language Processing and Recommender Systems

Python