This is a great book, and I'd say it is even great for those that are not familiar with python (you just obviously won't be able to run the code). For anyone with some basic understanding of linear algebra/statistics, the authors are able to present to you all the important (and sometimes subtle but significant) details, without the usage of equations, and more importantly, how they all relate to one another. All the concepts mentioned here are heavily backed with well thought of and well presented figures, in such a way that again I'd suggest you don't even need python to understand. If you do know python, loading the data sets and reproducing the figures is just a few lines of easy to understand code away (with the exception of the mglearn library includes which does some "plotting magic" for you. However, I believe each of them were appropriate.

My current work revolves around using machine learning for the study of criminal behavior, so I read Introduction to Machine Learning with Python by Andreas Muller and Sarah Guido with great interest. The book comprises a complete documentation of the scikit-learn library, and provides a comprehensive overview of the machine learning models and the fundamental theory needed to get started in applying ML tools in practice. Each chapter contains Python source code that cover a wide range of interesting and practical data science problems. In addition to the basic theory, scikit-learn tools and code samples, the book also includes many useful hints, tricks and words of wisdom that can save you a lot of time by avoiding issues that invariably arise in your learning process. This is an excellent book that I highly recommend both to machine learning experts who want to be proficient in scikit-learn and also to beginners who want to learn machine learning basics and how to apply them on data.