Machine Learning with Spark MLlib focuses on building and deploying scalable machine learning models using Apache Spark’s MLlib library. MLlib provides distributed algorithms for classification, regression, clustering, and recommendation systems that work efficiently on large datasets. This training explains how Spark handles data preprocessing, feature engineering, and model training in a distributed environment. It also covers pipelines for machine learning workflows, including data transformation, model selection, and evaluation. You will learn how to build end-to-end ML solutions that scale across clusters for big data applications. The course also highlights best practices for performance tuning, model optimization, and real-world use cases in data-driven systems.
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