Burkov A. The Hundred-Page Machine Learning Book

Andriy Burkov, 2019. - 160 p. - ISBN: 199957950X.Final version !
Completed on 5.7.2019The last twenty years have witnessed an explosion in the availability of enormous quantities of data and, correspondingly, of interest in statistical and machine learning applications. The impact has been profound. Ten years ago, when I was able to attract a full class of MBA students to my new statistical learning elective, my colleagues were astonished because our department struggled to fill most electives. Today we offer a Master’s in Business Analytics, which is the largest specialized master’s program in the school and has application volume rivaling those of our MBA programs. Our course offerings have increased dramatically, yet our students still complain that the classes are all full. Our experience is not unique, with data science and machine learning programs springing up at an extraordinary rate as the demand for individuals trained in this area has blossomed.This demand is driven by a simple, but undeniable, fact. Machine learning approaches have produced significant new insights in numerous settings such as the social sciences, business, biology and medicine, to name just a few. As a result, there is a tremendous demand for individuals with the requisite skill set. However, training students in these skills has been challenging because most of the early literature on these methods was aimed at academics and concentrated on statistical and theoretical properties of the fitting algorithms or resulting estimators. There was little support for researchers and practitioners who needed help in implementing a given method on real-world problems. These individuals needed to understand the range of methods that can be applied to each problem, along with their assumptions, strengths and weaknesses. But theoretical properties or detailed information on the fitting algorithms were far less important. Our goal when we wrote “An Introduction to Statistical Learning with R” (ISLR) was to provide a resource for this group. The enthusiasm with which it was received demonstrates the demand that exists within the community.“The Hundred-Page Machine Learning Book” follows a similar paradigm. As with ISLR, it skips involved theoretical derivations in favor of providing the reader with key details on how to implement the various approaches. This is a compact “how to do data science” manual and I predict it will become a go to resource for academics and practitioners alike. At 100 p. (or a little more), the book is short enough to read in a single sitting. Yet, despite its length, it covers all the major machine learning approaches, ranging from classical linear and logistic regression, through to modern support vector machines, deep learning, boosting, and random forests. There is also no shortage of details on the various approaches and the interested reader can gain further information on any particular method via the innovative companion book wiki. The book does not assume any high level mathematical or statistical training, or even programming experience, so should be accessible to almost anyone willing to invest the time to learn about these methods. It should certainly be required reading for anyone starting a Ph.D. program in this area and will serve as a useful reference as they progress further. Finally, the book illustrates some of the algorithms using Python code, one of the most popular coding languages for machine learning. I would highly recommend “The Hundred-Page Machine Learning Book” for both the beginner looking to learn more about machine learning, and the experienced practitioner seeking to extend their knowledge base.True PDF