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:
- Loading the data.
- Preprocessing, analyzing, and filtering the input data.
- Discovering patterns, affinities, clusters, and classes.
- Selecting the model features and the appropriate machine learning algorithm(s).
- Refining and validating the model.
- Improving the computational performance of the implementation.
As we will emphasize throughout this book, each stage of the process is critical to build the right model.
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.
