Building Probabilistic Graphical Models With Python
Solve machine learning problems using probabilistic graphical models implemented in Python with real-world applicationsAbout This BookStretch 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 NLPSolve real-world problems using Python libraries to run inferences using graphical m...
Paperback: 172 pages
Publisher: Packt Publishing - ebooks Account (June 25, 2014)
Product Dimensions: 7.5 x 0.4 x 9.2 inches
Amazon Rank: 1559249
Format: PDF Text djvu ebook
- 1783289007 epub
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- Kiran R Karkera epub
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“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 ins...”
delsA practical, step-by-step guide that introduces readers to representation, inference, and learning using Python libraries best suited to each taskWho This Book Is ForIf 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.What You Will LearnCreate Bayesian networks and make inferencesLearn the structure of causal Bayesian networks from dataGain an insight on algorithms that run inferenceExplore parameter estimation in Bayes nets with PyMC samplingUnderstand the complexity of running inference algorithms in Bayes networksDiscover why graphical models can trump powerful classifiers in certain problemsIn DetailWith 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.