Summary

In this chapter, you learned the fundamentals of what it means to be a successful machine learning practitioner and the skills necessary to build successful machine learning models. These require not only a broad set of requisite knowledge and experience but also a thorough understanding of the learning algorithms, the training dataset, the real-world deployment scenario, and the myriad ways that the work can go wrong—either by accident or by design.

The data science buzzword suggests a relationship between the data, the machine, and the people who guide the learning process. This is a team effort, and the growing emphasis on data science as a distinct outgrowth from the field of data mining that came before it, with numerous degree programs and online certifications, reflects its operationalization as a field of study concerned with not just statistics, data, and computer algorithms but also the technologic and bureaucratic infrastructure that enables applied machine...