This book is an introduction to the world of machine learning, a topic that is becoming more and more important, not only for IT professionals and analysts but also for all the data scientists and engineers who want to exploit the enormous power of techniques such as predictive analysis, classification, clustering, and natural language processing. In order to facilitate the learning process, all theoretical elements are followed by concrete examples based on Python.
A basic but solid understanding of this topic requires a foundation in mathematics, which is not only necessary to explain the algorithms, but also to let the reader understand how it's possible to tune up the hyperparameters in order to attain the best possible accuracy. Of course, it's impossible to cover all the details with the appropriate precision. For this reason, some topics are only briefly described, limiting the theory to the results without providing any of the workings. In this way, the user has the double opportunity to focus on the fundamental concepts (without too many mathematical complications) and, through the references, examine in depth all the elements that generate interest.
The chapters can be read in no particular order, skipping the topics that you already know. Whenever necessary, there are references to the chapters where some concepts are explained. I apologize in advance for any imprecision, typos or mistakes, and I'd like to thank all the Packt editors for their collaboration and constant attention.