Building Probabilistic Graphical Models with Python
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Building Probabilistic Graphical Models with Python
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Overview of this book
<p>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.</p>
<p>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.</p>
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Building Probabilistic Graphical Models with Python
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Table of Contents (15 chapters)
Building Probabilistic Graphical Models with Python
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Probability
Directed Graphical Models
Undirected Graphical Models
Structure Learning
Parameter Learning
Exact Inference Using Graphical Models
Approximate Inference Methods
References
Index
Customer Reviews