Chapter 1. Getting Started
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:
Defining the problem to solve.
Loading the data.
Preprocessing, analyzing, and filtering the input data.
Discovering patterns, affinities, clusters, and classes, if needed.
Selecting the model features and appropriate machine learning algorithm(s).
Refining and validating the model.
Improving the computational performance of the implementation.
In this book, each stage of the process is critical to build the right model.
Tip
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:
The Scala coding convention and standard used in the book in the Appendix A, Basic Concepts
API Scala docs
A fully documented source code that is available online
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.
