Who this book is for
The primary audience of this book is professionals who works with data and whose responsibilities may include data analysis, data visualization or transformation, the training, validation, testing and evaluation of machine learning models—presumably to perform predictive, descriptive or prescriptive analytics using Java or Java-based tools. The choice of Java may imply a personal preference and therefore some prior experience programming in Java. On the other hand, perhaps circumstances in the work environment or company policies limit the use of third-party tools to only those written in Java and a few others. In the second case, the prospective reader may have no programming experience in Java. This book is aimed at this reader just as squarely as it is at their colleague, the Java expert (who came up with the policy in the first place).
A secondary audience can be defined by a profile with two attributes alone: an intellectual curiosity about machine learning and the desire for a single comprehensive treatment of the concepts, the practical techniques, and the tools. A specimen of this type of reader can opt to skip the math and the tools and focus on learning the most common supervised and unsupervised learning algorithms alone. Another might skim over Chapters 1, 2, 3, and 7, skip the others entirely, and jump headlong into the tools—a perfectly reasonable strategy if you want to quickly make yourself useful analyzing that dataset the client said would be here any day now. Importantly, too, with some practice reproducing the experiments from the book, it'll get you asking the right questions of the gurus! Alternatively, you might want to use this book as a reference to quickly look up the details of the algorithm for affinity propagation (Chapter 3, Unsupervised Machine Learning Techniques), or remind yourself of an LSTM architecture with a brief review of the schematic (Chapter 7, Deep Learning), or dog-ear the page with the list of pros and cons of distance-based clustering methods for outlier detection in stream-based learning (Chapter 5, Real-Time Stream Machine Learning). All specimens are welcome and each will find plenty to sink their teeth into.
