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Trent Hauck

Scikit-Learn Cookbook Paperback – November 4, 2014

by Trent Hauck (Author)

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scikit-learn Cookbook - Second Edition: Over 80 recipes for machine learning in Python with scikit-learn
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Scikit-Learn Cookbook

Print length

214 pages
Language

English
Publisher

Packt Publishing
Publication date

November 4, 2014
Dimensions

7.5 x 0.49 x 9.25 inches
ISBN-10

1783989483
ISBN-13

978-1783989485
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Product details

Publisher ‏ : ‎ Packt Publishing (November 4, 2014)
Language ‏ : ‎ English
Paperback ‏ : ‎ 214 pages
ISBN-10 ‏ : ‎ 1783989483
ISBN-13 ‏ : ‎ 978-1783989485
Item Weight ‏ : ‎ 13.3 ounces
Dimensions ‏ : ‎ 7.5 x 0.49 x 9.25 inches

Customer Reviews:
9 ratings

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mgustav

Pretty much useless compared to the scikit-learn documentation

Reviewed in the United States on November 17, 2016

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Nope. The author of scikit-learn did a much better job explaining how to use it (Introduction to Machine Learning with Python by Andreas Muller).
This book, despite being a cookbook, is hard to follow and hard to read.

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Ryan

A useful text on how to use sci-kit learn in practice (and learn a bit about how the algorithms work)

Reviewed in the United States on April 13, 2015

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Higher resolution review: 4.25 - 4.5 stars

I bought this e-book to help prepare for a university regression-based machine learning project, and it was tremendously helpful to me as someone who was not so experienced using machine learning in practice. I found the style to be somewhere between a 'cookbook' and a more general 'how-to'. I should mention that I've read the regression, preprocessing, and post-model work flow sections quite closely and followed the examples, but I haven't yet spent much time on the distance metrics or classification chapters. In particular, the following are strong points in my opinion:

i) There is enough theory to give readers an idea of what's going on under the hood, but not so much that one feels as if they're slogging through a mathematical morass.

ii) There's a nice inclusion of the steps that I would imagine are usually a part of most machine learning pipelines.

iii) For a non-numpy expert, I found the examples easy to follow, and useful for getting a hang of using sci-kit learn (and numpy, to some extent).

iv) There's an emphasis on highlighting how various tools in sci-kit learn are useful/aplicable on actual data. This is quite useful if you have some if your own data on hand.

v) Beyond simple enumerating all of the different regression (for e.g.) capabilities in sci-kit learn, the author includes useful explanations to show how they relate to one another and how they might be useful/advantageous in certain situations.

vi) Along the way, useful tricks involving scikit-learn/numpy are shown. I'll definitely use these in the future.

vii) I found the writing style colloquial and enjoyable to read

And the reasons for only 4/5:

i) I personally would have liked to see more 'putting it together sections'

ii) I found some explanations a little too brief

iii) Typos here and there (mostly minor)

iv) There are some references to sections which have not been mentioned yet.

As with most of these books, most of the information in them is probably out there somewhere on forums, etc., but having a lot of it condensed in one place with succinct explanations is quite valuable for saving time. In my opinion, this alone makes the book worth it (although I would have preferred a Kindle price closer to $10; the price is $16.54 at the time of writing).

The people who would benefit most from this book, I think, are those who (like me) have a background in python, but not so much in machine learning. Overall, the book did a good job of getting me comfortable using the sci-kit learn package, and I will likely refer back in to it in future machine learning projects.

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GEORGE R. FISHER

Disappointing

Reviewed in the United States on July 28, 2015

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Most scikit learn books are 'introductory', this one holds out the hope of being written for people who know what sklearn is and it does achieve that. However, it is very poorly written; it looks like it was written in a great rush to get published and that greatly detracts from its value. By and large, YouTube is a better source.

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A. Zubarev

A tad dry, technical read but with a huge potential

Reviewed in the United States on February 14, 2015

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.

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Scikit-Learn Cookbook Paperback – November 4, 2014

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