Book Image

Machine Learning Quick Reference

By : Rahul Kumar
Book Image

Machine Learning Quick Reference

By: Rahul Kumar

Overview of this book

Machine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques. This book guides you to do just that in a very compact manner. After giving a quick overview of what machine learning is all about, Machine Learning Quick Reference jumps right into its core algorithms and demonstrates how they can be applied to real-world scenarios. From model evaluation to optimizing their performance, this book will introduce you to the best practices in machine learning. Furthermore, you will also look at the more advanced aspects such as training neural networks and work with different kinds of data, such as text, time-series, and sequential data. Advanced methods and techniques such as causal inference, deep Gaussian processes, and more are also covered. By the end of this book, you will be able to train fast, accurate machine learning models at your fingertips, which you can easily use as a point of reference.
Table of Contents (18 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
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...