Book Image

Mastering Java Machine Learning

By : Uday Kamath, Krishna Choppella
Book Image

Mastering Java Machine Learning

By: Uday Kamath, Krishna Choppella

Overview of this book

Java is one of the main languages used by practicing data scientists; much of the Hadoop ecosystem is Java-based, and it is certainly the language that most production systems in Data Science are written in. If you know Java, Mastering Machine Learning with Java is your next step on the path to becoming an advanced practitioner in Data Science. This book aims to introduce you to an array of advanced techniques in machine learning, including classification, clustering, anomaly detection, stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, deep learning, and big data batch and stream machine learning. Accompanying each chapter are illustrative examples and real-world case studies that show how to apply the newly learned techniques using sound methodologies and the best Java-based tools available today. On completing this book, you will have an understanding of the tools and techniques for building powerful machine learning models to solve data science problems in just about any domain.
Table of Contents (20 chapters)
Mastering Java Machine Learning
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Linear Algebra
Index

Bayesian networks


Generally, all Probabilistic Graphical Models have three basic elements that form the important sections:

  • Representation: This answers the question of what does the model mean or represent. The idea is how to represent and store the probability distribution of P(X1, X2, …. Xn).

  • Inference: This answers the question: given the model, how do we perform queries and get answers. This gives us the ability to infer the values of the unknown from the known evidence given the structure of the models. Motivating the main discussion points are various forms of inferences involving trade-offs between computational and correctness concerns.

  • Learning: This answers the question of what model is right given the data. Learning is divided into two main parts:

    • Learning the parameters given the structure and data

    • Learning the structure with parameters given the data

We will use the well-known student network as an example of a Bayesian network in our discussions to illustrate the concepts and...