The book provides a super fast, short introduction to Spark in the first chapter and then jump straight into MLlib, Spark Streaming Spark SQL, GraphX, etc. in subsequent chapters. A huge positive for this book is that it not only talks about Spark itself, but also covers using Spark with other big data technologies like Hadoop, Kafka, Titan, Neo4j, HBase, Cassandra, H2O, etc. True to the name, sure the book covers more than simple introductory Spark topics, but it concentrates on breath than depth. There is decent coverage and enough code examples for each topic, but what it lacks is depth. There is no "best practices" or "performance" or "watch out for" type discussions or any type of advanced code. The MLlib chapter covers Naive Bayes, K-Means and Artificial Neural Networks (ANN).