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  • 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

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.

  • 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
Page Count 172
Course Length 5 hours 9 minutes
ISBN 9781783289004
Date Of Publication 24 Jun 2014


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.