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Machine Learning with Spark - Second Edition by Nick Pentreath, Manpreet Singh Ghotra, Rajdeep Dua

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MLlib supported methods and developer APIs

MLlib provides fast and distributed implementations of learning algorithms, including various linear models, Naive Bayes, SVM, and Ensembles of Decision Trees (also known as Random Forests) for classification and regression problems, alternating.

Least Squares (explicit and implicit feedback) are used for collaborative filtering. It also supports k-means clustering and principal component analysis (PCA) for clustering and dimensionality reduction.

The library provides some low-level primitives and basic utilities for convex optimization (http://spark.apache.org/docs/latest/mllib-optimization.html), distributed linear algebra (with support for Vectors and Matrix), statistical analysis (using Breeze ...

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