Practical machine learning with Python: a problem-solver's guide to building real-world intelligent systems

摘要

The book's title explicitly states its main purpose: to provide a practical problem-solving guide for those who seek to build real-world applications. The book consists of 12 chapters divided into three parts, the first two dealing with fundamentals and the third with practical real-life cases. Fundamentals comprise the first two chapters of the book. The first chapter provides an extensive overview of machine learning (ML) from technical and practical points of view, without any formal proofs or theories. In this chapter, the authors discuss the data-driven decision-making process, ML as disruptive programming, and various fundamental concepts from mathematics, computer science, and data science, and they enumerate basic ML methods (supervised, unsupervised, reinforcement learning, and so on). It is important to understand that although the chapter provides an excellent overview of all these fundamental ML concepts, techniques, and models, it is not intended to be a comprehensive tutorial; rather, it is a guide to what one needs to master before jumping into the development of practical data-driven systems. The style is light, clean, and very informative. In addition, this part provides a detailed description of the cross-industry standard process for data mining (CRISP-DM) methodology commonly used for planning data mining projects. All of this not only allows the reader to touch on the actual mechanics of a data-driven project, but also to get up to speed with regards to building actual ML pipelines for supervised and unsupervised learning. The chapter concludes with a small real-life project that serves as a self-test and summary for anyone who wants a quick start in applications development. The second chapter is an introduction to Python. Again, the chapter is not a comprehensive tutorial; however, it gives a good overview of Python in the context of ML.