It is critical for any computer scientist to understand the different classes of machine learning algorithms and be able to select the ones that are relevant to the domain of their expertise and dataset. However, the application of these algorithms represents a small fraction of the overall effort needed to extract an accurate and performing model from input data. A common data mining workflow consists of the following sequential steps:
In this book, each stage of the process is critical to build the right model.
It is impossible to describe the key machine learning algorithms and their implementations in detail in a single book. The sheer quantity of information and Scala code would overwhelm even the most dedicated readers. Each chapter focuses on the mathematics and code that are absolutely essential to the understanding of the topic. Developers are encouraged to browse through the following:
This first chapter introduces you to the taxonomy of machine learning algorithms, the tools and frameworks used in the book, and a simple application of logistic regression to get your feet wet.
Each chapter contains a small section dedicated to the formulation of the algorithms for those interested in the mathematical concepts behind the science and art of machine learning. These sections are optional and defined within a tip box. For example, the mathematical expression of the mean and the variance of a variable X mentioned in a tip box will be as follows: