Building Probabilistic Graphical Models with Python

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

Building Probabilistic Graphical Models with Python

Kiran R Karkera

6 customer reviews
Solve machine learning problems using probabilistic graphical models implemented in Python, with real-world applications
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Book Details

ISBN 139781783289004
Paperback172 pages

Book Description

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

  • 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

Authors

Book Details

ISBN 139781783289004
Paperback172 pages
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