This course equips participants with the knowledge and relevant skills required to build, train, and deploy scalable machine learning models using distributed computing. It introduces the core concepts of Spark’s architecture, explores data preprocessing and feature engineering at scale, and covers supervised and unsupervised learning techniques implemented with MLlib. The course emphasizes practical applications, performance optimization, and integration of Spark with real world AI pipelines, equipping participants to handle large scale datasets and deliver efficient, production ready machine learning solutions.