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

Chapter 8. Probabilistic Graphical Models

Before we get into Bayesian network (BN) concepts, we should be aware of the theories of probability. So, we will try to touch upon them and build the foundation of BNs.

We already know that probability is the degree of certainty/uncertainty of an event occurring. However, it can be also termed as the degree of belief, which is more commonly used when we talk about BN.

When we toss a fair coin, we say that the degree of belief around the event of heads/tails happening is 0.5. It implies that our belief of heads happening is as strong as tails. The probability can be seen as follows:

p(Heads)=p(tails)=0.5

In this chapter, we will cover the following topics:

  • Bayesian rules
  • Bayesian networks