What this book covers

Chapter 1, Introducing Apache Mahout, provides an introduction to machine learning and Apache Mahout.

Chapter 2, Clustering, provides an introduction to unsupervised learning and clustering techniques (K-Means clustering and other algorithms) in Apache Mahout along with performance optimization tips for clustering.

Chapter 3, Regression and Classification, provides an introduction to supervised learning and classification techniques (linear regression, logistic regression, Naïve Bayes, and HMMs) in Apache Mahout.

Chapter 4, Recommendations, provides a comparison between collaborative- and content-based filtering and recommenders in Apache Mahout (user-based, item-based, and matrix-factorization-based).

Chapter 5, Apache Mahout in Production, provides a guide to scaling Apache Mahout in the production environment with Apache Hadoop.

Chapter 6, Visualization, provides a guide to visualizing data using D3.js.