Table of Contents

Chapter 1:  Introduction to Spark 3.1.- Chapter 2:  Manage Data with PySpark.- Chapter 3: Introduction to Machine Learning.- Chapter 4: Linear Regression with PySpark.- Chapter 5: Logistic Regression with PySpark.- Chapter 6: Ensembling with PySpark.- Chapter 7: Clustering with PySpark.- Chapter 8: Recommendation Engine with PySpark.- Chapter 9: Advanced Feature Engineering with PySpark.

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. He’s 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 O’Reilly’s 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 IIM–Calcutta. 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.