scikit-learn Cookbook by Trent Hauck, Packt Publishing Book Review
This book was released back in Fall 2014, but I did not had a chance to read it until recently. A big miss. As far as I can tell, it is the one of the few books covering as much ground as possible in concern to scikit-learn as free Machine Learning (ML) libraries available for Python. In general, the Machine Learning is a fascinated piece of science seeing a lot of traction these days, but it is a tad intimidating to grasp at the beginning, besides, its potential use cases given it fallen into the wrong hands (g-d forbid) can be scary. Otherwise I foresee a huge potential for it’s use in the IOT.
This book aims at easing the ML adoption hurdles providing with not less than 50 recipes which cover pretty much the whole scikit-learn landscape. I could see Trent made every effort to deliver a hight quality product. The book has a supplementary file that covers what an end user needs to install to go through all the material in the book and obtain sample data.
In terms of a general note, since this product is aiming at mostly the data scientist, engineers or research staff many topics are not going to be quite familiar to a wide non-technical or general IT audience, but please ensure you put an extra effort in understanding the concepts. Like I have said, the benefits are enormous. And prepare yourself to scratch your head a few times or more :-). Yes, this is a very advanced book. Yet, it seems that it covers all the possible scenarios and industry fields one can imagine off. Numerous graphics, detailed code samples and output examples, all are ready to copy and paste into the mighty Python REPL.
When I was reading the book I had a task at hand and I concentrated on the KMeans algorithm which is elegantly covered, and I enjoyed the most the chapter on Classifying Data. At the same time I think the cornerstone of the book is chapter 1 on pre-model workflow and the last on the post-model, I just did not see books to date going this far.
While this book is more like an ‘Academia’ publication it does have many practical applications, but for a less Data Science savvy person it desires to have more explanation on why XYZ and ABCs are necessary, or what each library function is used for and under what circumstances one would choose to use it.
Overall it is a tad dry, technical read, but at the same time no extra, volume inflating words were mixed in, so it is worth what you are paying for.
My verdict, it 4.5 our of 5.