Machine learning with PySpark : with natural language processing and recommender systems

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

This book covers topics such as building scalable machine learning models, natural language processing, and recommender systems. It covers the fundamentals of Apache Spark, traditional machine learning algorithm implementations, and data ingestion and processing to solve business problems. The book demonstrates how to build supervised models like linear regression, logistic regression, decision trees, and random forests, and how to automate these steps using Spark pipelines. It also introduces Koalas in Spark and how to automate data workflow using Airflow and PySpark's latest ML library. The book aims to help developers build and train various machine learning models, understand data processing using Koalas in Spark, and handle issues like feature engineering, class balance, bias and variance, and cross validation.