Machine Learning with Databricks and Spark MLlib focuses on building scalable machine learning solutions using the Databricks platform and Apache Spark’s MLlib library. Spark MLlib provides distributed algorithms for classification, regression, clustering, recommendation systems, and predictive analytics. This training explains how Databricks supports collaborative model development, data preprocessing, feature engineering, and large-scale model training. It also covers machine learning pipelines, model evaluation, and workflow automation using Spark-based processing. You will learn how to develop and optimize machine learning models that handle massive datasets efficiently in distributed environments. The course also highlights best practices for scalable AI workflows, performance tuning, and enterprise-level analytics solutions.
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