The conscientious reader should carefully heed the words of this title. The book functions as an introduction to Python that leads, step by step, to using it with Machine Learning. It takes the shortest and easiest path to get there. As such, it provides fairly immediate gratification for the reader. With such low-hanging fruit, its intended audience is beginners without a lot of knowledge in computer science. For that audience, Anis effectively communicates his message. My only concern is that it does not provide many ways to gain depth, through suggested resources, appendices, footnotes/endnotes, etc.

This book helps beginners get over the initial fear of programming and machine learning. These terms are thrown about casually, and this book demystifies them. However, it does not veer from that trail almost at all. An interested reader will surely need to purchase two other books to read after Anis’ work: a book on Python and a book on Machine Learning. This book can serve as a high-level overview, but more in-depth knowledge is needed to leverage machine learning’s and Python’s strengths towards particular problems.

As a computer programmer and as a writer, I appreciate how quickly Anis gets us up to speed with Python and with basic machine learning concepts. He clearly communicates with little technical jargon. He describes his concepts simply and elegantly. He deserves much laudatory credit for this accomplishment. In addition, his choice of Python serves his purposes well. In my professional work, I’ve dealt with these more with the R programming/statistics language. Python certainly seems easier and more suited for beginners.

Overall, this book is well-written, has code snippets, and has good graphics. However, it must serve as one resource among many. It lacks a comprehensive treatment of the matter or even suggestions on how to access one. Anyone looking to do serious work and depth needs to spend time using other resources. Besides beginning data scientists, this work could help business leadership identify specific technical opportunities in the Python-Machine Learning combination. This book whet my appetite, but I am still hungry for the main course. ( )