Learn the fundamentals of machine learning with Apache Spark™ in this comprehensive tutorial that covers distributed learning concepts, data import and exploratory analysis techniques, and the core components of Spark's ML framework including transformers, estimators, and pipelines. Explore featurization methods for preparing data for machine learning algorithms, then advance to model training techniques and interpretation strategies to understand and evaluate your results. Master the essential skills needed to implement scalable machine learning solutions using Apache Spark's distributed computing capabilities across five structured modules designed to build your expertise progressively.

Syllabus

Apache Spark™ ML and Distributed Learning (1/5)
Data Import and Exploratory Analysis (2/5)
Transformers, Estimators, and Pipelines (3/5)
Featurization (4/5)
Model Training and Interpretation (5/5)