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Machine Learning Quick Reference

You're reading from Machine Learning Quick Reference

Product type Book

Published in Jan 2019

Publisher Packt

ISBN-13 9781788830577

Pages 294 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Author (1):

Rahul Kumar

Table of Contents (18) Chapters

Title Page

Copyright and Credits

About Packt

Contributors

Preface

1. Quantifying Learning Algorithms

2. Evaluating Kernel Learning

3. Performance in Ensemble Learning

4. Training Neural Networks

5. Time Series Analysis

6. Natural Language Processing

7. Temporal and Sequential Pattern Discovery

8. Probabilistic Graphical Models

9. Selected Topics in Deep Learning

10. Causal Inference

11. Advanced Methods

1. Other Books You May Enjoy

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Index

Bayes network

Bayes network is a type of probabilistic graphical model that can be used to build models to address business problems. Applications of this are quite wide. For example, it can be used in anomaly detection, predictive modeling, diagnostics, automated insights, and many other applications.

It is totally understandable that a few words used here would have been alien to you till now. For example, what do we mean by graphical here?

A graph forms out of a set of nodes and edges. Nodes are represented by N={N1,N2…..Nn}, where independent variables are sitting at every node. Edges are the connectors between nodes. Edges can be denoted by E={E1, E2…..En} and can be of two types:

Directed, represented by
Undirected, represented by:

With the help of nodes and edges, a relationship between the variables is exhibited. It can be a conditional independence relationship or a conditional dependence relationship. BN is one a techniques that can introduce causality amongst variables. Although...

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Authors (1)

Rahul Kumar

Rahul Kumar has got more than 10 years of experience in the space of Data Science and Artificial Intelligence. His expertise lies in the machine learning and deep learning arena. He is known to be a seasoned professional in the area of Business Consulting and Business Problem Solving, fuelled by his proficiency in machine learning and deep learning. He has been associated with organizations such as Mercedes-Benz Research and Development (India), Fidelity Investments, Royal Bank of Scotland among others. He has accumulated a diverse exposure through industries like BFSI, telecom and automobile. Rahul has also got papers published in IIM and IISc Journals.

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