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Kiran R Karkera

Building Probabilistic Graphical Models with Python Paperback – June 25, 2014

by Kiran R Karkera (Author)

11 ratings

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Building Probabilistic Graphical Models with Python

Print length

172 pages
Language

English
Publisher

Packt Publishing
Publication date

June 25, 2014
Dimensions

7.5 x 0.39 x 9.25 inches
ISBN-10

1783289007
ISBN-13

978-1783289004
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Product details

Publisher ‏ : ‎ Packt Publishing; Illustrated edition (June 25, 2014)
Language ‏ : ‎ English
Paperback ‏ : ‎ 172 pages
ISBN-10 ‏ : ‎ 1783289007
ISBN-13 ‏ : ‎ 978-1783289004
Item Weight ‏ : ‎ 13.6 ounces
Dimensions ‏ : ‎ 7.5 x 0.39 x 9.25 inches

Best Sellers Rank: #3,073,432 in Books (See Top 100 in Books)
- #1,865 in Programming Algorithms
- #3,286 in Python Programming
- #16,931 in Computer Science (Books)

Customer Reviews:
11 ratings

About the author

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Kiran R Karkera

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As a Data Scientist and engineer, I am enthusiastic about using Machine Learning and Natural Language processing tools to build data products that solve challenging problems.

I curate and design data products, as well as implement the core algorithms in NLP and Machine Learning.

As an applied machine learning engineer, I have wide exposure to both statistical as well as machine learning techniques. I have open sourced a few algorithms in NLP as well as tools for unsupervised text clustering. I've participated in several global Machine Learning competitions, and I have been placed among the top 1% of data scientists at Kaggle.com (as of Dec 2013).

I have filed 2 patents that help improve Femtocell Devices and Smallcell Device Management Server efficiency using Machine Learning approaches.

Customer reviews

5 star		15%
4 star		15%
3 star 0% (0%)		0%
2 star		21%
1 star		50%

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Top reviews from the United States

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M. Athar

outdated, incomplete

Reviewed in the United States on February 21, 2019

Verified Purchase

Poorly written, riddled with errors, uses a package that hasn't been updated since 2015 (possibly abandoned?)

One person found this helpful

Helpful

Sidney

Kindle edition unusable

Reviewed in the United States on September 6, 2015

Verified Purchase

All the equations in the Kindle edition are scrambled, making the book unusable. It may be a good book in the print edition.

One person found this helpful

Helpful

Charles Saunders

Riddled With Programming Errors

Reviewed in the United States on July 28, 2017

Verified Purchase

If you are an inexperienced programmer or new to Python/Jupyter/Anaconda DO NOT BUY THIS OR ANY OTHER PACKT Publishing book as the code contains errors that are difficult to rectify. Packt Publishing DOES NOT verify code like the CRC Press - for instance, Bayesian Networks in R by CRC press - i.e. Probabilistic Graphical Models in R - has code AND content that is helpful for both the academic and practitioner. Also, the Packt Publishing description/outline of probability is weak, at best, and is confusing to many of my students.

If correcting Python code is not a big deal for you then Packt books are a nice intro. But why buy something that you have to fix before you can start working with it?

One person found this helpful

Helpful

Ace of Space is the place

Four Stars

Reviewed in the United States on November 29, 2014

Verified Purchase

good coverage of a new statistical analytic method

Helpful

Tungsten

All Equations and Code are Unreadable Gibberish on Kindle Version - Do Not Buy!

Reviewed in the United States on July 12, 2015

This review is for the Kindle version only, I have not read the print version.
This is a book concerning math, as such it has lots of formulas.
Those formulas are completely scrambled and mostly undecipherable on all platforms.
I have included a screen shot of a typical page.
I have tried reading this book on my Android phone, iPad, PC desktop, and Macbook Pro. It is unreadable on all platforms.
There is no way anyone who actually tried reading this book on Kindle would find it useful.
I would therefore also strongly question the veracity of the reviews of the Kindle version which don't mention the fact that the Kindle version is unreadable.
The text portions of the book seem reasonable and logical, generally well written.
Might be a good book to own if the publisher would fix it, but there is no way to really tell.

19 people found this helpful

Helpful

ar66

Very poor

Reviewed in the United States on November 28, 2019

I was deeply disappointed by this book. DO NOT BUY! I started off wanting to like it (of course), but it's infested with grammatical & logical errors, literally starting on page 1. It reads as if it weren't edited at all. The topics are poorly explained and crucial details are omitted. Trying to learn anything from it is an exercise in frustration.

Helpful

satnam singh

Useful Book for Programmers who want to use Graphical Models in Python

Reviewed in the United States on August 31, 2014

“Building Probabilistic Graphical Models (PGMs) with Python” book is an excellent pick up for programmers who just want to know basics of the PGMs and quickly apply them to solve their analytical problems. Book’s author, Kiran has done an excellent work in collecting knowledge about the PGMs from multiple places and providing it in a simple and lucid form.

The book provides detailed python code to solve almost all the analytical problems of the PGMs including both approximate and exact inference computation, structure learning and parameter learning. The book does provide necessary mathematics and theory along with Python code.

On the flip side, the book assumes that reader should know what kind of problems the PGMs can solve. The focus of the book is to provide adequate technical details of the PGMs and python code so that anybody can start using it.

I would say that this book is one stop buy for anyone who quickly wants to put hands dirty and start using to solve their analytical problems.

10 people found this helpful

Helpful

MR GRAMFORT ALEXANDRE

Perfect to get started with probabilistic graphical models in Python !

Reviewed in the United States on August 5, 2014

This book is perfect to get you started with probabilistic graphical models (PGM) with Python. It starts with a quick intro to Bayesian and Markov Networks covering concepts like conditional independence and D-separation. It then covers the different aspects of PGM: structure learning, parameter estimation (with frequentist or Bayesian approach) and inference. All is illustrated with examples and code snippets using mostly the libpgm package. PyMC is used for Bayesian parameter estimation.

It is fairly well written although there is a few typos here and there as well as little formatting errors in the python code.

It will not make you an expert of PGM as the book by Daphne Koller but it will get you started quickly with Python.

Definitely an enjoyable read if you're interested in PGM with Python.

9 people found this helpful

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Top reviews from other countries

Amazon Customer

Would benefit from some professional edition

Reviewed in the United Kingdom on November 2, 2015

Verified Purchase

Plenty of typos and omitted details make it very difficult to follow at times.

Useful to hit the ground running and get your hands dirty, though.

Beebear

Careless

Reviewed in Germany on August 17, 2014

Verified Purchase

This book presents a quick overview of practical graphical network models and some Python libraries for it.
The choice of topics is good, but the effort put into conveying them seems low. The reasoning is poorly structured so that it is hard to learn something new. Some tables and figures show obvious signs of draft versions. The author couldn't be bothered to provide correct resolution for all the plots. Thus, some plots are upscaled from very low resolution with crammed labels and a blurred, illegible font. The work features a basic miss-interpretation of the p-value. Some special paragraphs are missing or shifted, so it's hard to believe that the author did proof-read the work even once.
While such a book is a nice idea, the current state is not a book worth paying for.

6 people found this helpful

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