Building Probabilistic Graphical Models with Python

by Kiran R Karkera

This is a short, practical guide that allows data scientists to understand the concepts of Graphical models and enables them to try them out using small Python code snippets, without being too mathematically complicated. If you are a data scientist who knows about machine learning and want to enhance your knowledge of graphical models, such as Bayes network, in order to use them to solve real-world problems using Python libraries, this book is for you.This book is intended for those who have some Python and machine learning experience, or are exploring the machine learning field.

Genres: Programming

155 pages, Paperback

First published January 1, 2014

Reviews

[Ji · Rating 1 out of 5]

For a book that's written solely for a new python package called libpgm, it's tragic that the provided code doesn't really work, probably because the package has evolved a lot since the book was written. However it's the package itself that's folding data into a specific shape because the "modeling" work can be done that really throw me off.. Not to mention that the book seems to be pieced together by one half simple statistics and one half lippgm based python codes. I gave it up quickly.

[Petr · Rating 2 out of 5]

Although a reasonable summary addressing probabilistic graphical models, it lacks some unified approach that would allow the reader to grasp the concepts and introduced mechanisms easier. The references section maybe shows why this is the case - each chapter, although ordered approximately according to growing complexity, is strongly based on its references but seemingly without a bigger attempt to generalize or summarize the subject.

I cannot recommend it neither as a reference book nor as a good complete introduction to the subject in general. Some chapters are useful and thus the book still has its value, but I will be certainly looking for some better source.

[Randy · Rating 2 out of 5]

Badly written Python code roughly following the popular Coursera class on PGMs using an unsupported and apparently abandon library. Interns could have done better.

[Tyler · Rating 3 out of 5]

Some of the packages used are no longer maintained. Still a good book to get the gist of PGMs, but a better reference text should be a companion.