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Mining Data from Variable Dependencies

This course will teach you several models like Bayesian Networks, LBP, Variable Elimination, etc. with the help of which you can derive complex relationships across multiple input variables or features.

Niraj Joshi - Pluralsight course - Mining Data from Variable Dependencies
by Niraj Joshi

What you'll learn

Mining data involves deriving complex probabilistic relationships between multiple variables. In this course, Mining Data from Variable Dependencies, you’ll learn to apply probabilistic graph models to derive complex relationships across variables/features. First, you’ll explore Bayesian Networks. Next, you’ll discover D Separation. Finally, you’ll learn how to perform data fragmentation. When you’re finished with this course, you’ll have the skills and knowledge of Python Probabilistic models needed to explore relationships across variables/input features to derive joint probabilities, or impact of features on the final outcome.

Table of contents

About the author

Niraj Joshi - Pluralsight course - Mining Data from Variable Dependencies
Niraj Joshi

Niraj has extensive experience with coding, architecting and consulting experience with data warehousing/ artificial intelligence/ machine learning/ visualization skillsets.

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