Probabilistic graph models (PGMs), also known as graph models, capture the relationship between different variables and represent the probability distributions. PGMs capture joint probability distributions and can be used to answer different queries and make inferences that allow us to make predictions on unseen data. PGMs have the great advantage of capturing domain knowledge of experts and the causal relationship between variables to model systems. PGMs represent the structure and they can capture knowledge in a representational framework that makes it easier to share and understand the domain and models. PGMs capture the uncertainty or the probabilistic nature very well and are thus very useful in applications that need scoring or uncertainty-based approaches. PGMs are used in a wide variety of applications that use machine learning such as applications to domains of language processing, text mining and information extraction, computer vision,...
Mastering Java Machine Learning
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Mastering Java Machine Learning
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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.
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Current Title:
Mastering Java Machine Learning
Table of Contents (20 chapters)
Mastering Java Machine Learning
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Free Chapter
Machine Learning Review
Practical Approach to Real-World Supervised Learning
Unsupervised Machine Learning Techniques
Semi-Supervised and Active Learning
Real-Time Stream Machine Learning
Probabilistic Graph Modeling
Deep Learning
Text Mining and Natural Language Processing
Big Data Machine Learning – The Final Frontier
Linear Algebra
Probability
Index
Customer Reviews