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Building Probabilistic Graphical Models with Python [Englisch] [Taschenbuch]

Kiran R Karkera
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25. Juni 2014

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


  • Stretch the limits of machine learning by learning how graphical models provide an insight on particular problems, especially in high dimension areas such as image processing and NLP
  • Solve real-world problems using Python libraries to run inferences using graphical models
  • A practical, step-by-step guide that introduces readers to representation, inference, and learning using Python libraries best suited to each task

In Detail

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.

What you will learn from this book

  • Create Bayesian networks and make inferences
  • Learn the structure of causal Bayesian networks from data
  • Gain an insight on algorithms that run inference
  • Explore parameter estimation in Bayes nets with PyMC sampling
  • Understand the complexity of running inference algorithms in Bayes networks
  • Discover why graphical models can trump powerful classifiers in certain problems


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.

Who this book is written for

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.


  • Taschenbuch: 172 Seiten
  • Verlag: Packt Publishing (25. Juni 2014)
  • Sprache: Englisch
  • ISBN-10: 1783289007
  • ISBN-13: 978-1783289004
  • Größe und/oder Gewicht: 0,9 x 18,8 x 23,1 cm
  • Durchschnittliche Kundenbewertung: 2.0 von 5 Sternen  Alle Rezensionen anzeigen (1 Kundenrezension)
  • Amazon Bestseller-Rang: Nr. 175.190 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)

Mehr über den Autor

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Über den Autor und weitere Mitwirkende

Kiran R Karkera

Kiran R Karkera is a telecom engineer with a keen interest in machine learning. He has been programming professionally in Python, Java, and Clojure for more than 10 years. In his free time, he can be found attempting machine learning competitions at Kaggle and playing the flute.

In diesem Buch (Mehr dazu)
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Die hilfreichsten Kundenrezensionen
2.0 von 5 Sternen Careless 17. August 2014
Format:Taschenbuch|Verifizierter Kauf
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.
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Die hilfreichsten Kundenrezensionen auf (beta) 4.0 von 5 Sternen  2 Rezensionen
4.0 von 5 Sternen Useful Book for Programmers who want to use Graphical Models in Python 31. August 2014
Von satnam singh - Veröffentlicht auf
“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.
4.0 von 5 Sternen Perfect to get started with probabilistic graphical models in Python ! 5. August 2014
Von MR GRAMFORT ALEXANDRE - Veröffentlicht auf
Format:Kindle Edition
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.
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