Building Probabilistic Graphical Models with Python

by Kiran R Karkera

Solve machine learning problems using probabilistic graphical models implemented in Python with real-world applications



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.






With the increasing prominence in machine learning and data science applications, probabilistic graphical models are a new tool that machine learning users can use to discover and analyze structures in complex problems. The variety of tools and algorithms under the PGM framework extend to many domains such as natural language processing, speech processing, image processing, and disease diagnosis.
You've probably heard of graphical models before, and you're keen to try out new landscapes in the machine learning area. This book gives you enough background information to get started on graphical models, while keeping the math to a minimum.

Genres: Programming

259 pages, Kindle Edition

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.