This project examines Apache Spark’s ML library algorithms, explaining their internal mechanics and distributed implementations. It covers topics including data types, statistical operations, collaborative filtering with ALS, classification models like SVMs and naive Bayes, regression techniques including linear and generalized linear models, decision trees and ensemble methods like random forests and gradient boosting, as well as clustering and dimensionality reduction algorithms. The content serves as a learning resource for developers seeking to understand how ML algorithms are implemented in distributed computing environments.