Introduction to Spark Machine Learning with PySpark

As data volumes grow, traditional single-machine machine learning libraries hit their limits. Spark provides the distributed computing power needed to train models on massive datasets, and PySpark MLlib makes this power accessible. In this text-only course, you will transition from fundamental distributed computing concepts to building and evaluating scalable machine learning pipelines. What you'll learn: • Learn the core architecture of Spark and how distributed data processing works. • Understand how to prepare and transform large datasets using PySpark DataFrames. • Build machine learning pipelines using PySpark MLlib for classification and regression. • Apply feature engineering techniques like vector assembly, scaling, and string indexing. • Evaluate model performance using distributed evaluation metrics and cross-validation. • Configure and save trained models for future inference and basic MLOps workflows. You will start with essential terminology and the foundational mechanics of Spark's distributed architecture. From there, you will progress through detailed written explanations and code snippets, learning how to clean data, engineer features, and train models step-by-step. This course is designed for beginners to distributed computing. Basic familiarity with Python is helpful, but no prior experience with Spark or machine learning is required. Start reading today to master scalable machine learning with PySpark.