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Mastering Java Machine Learning

Mastering Java Machine Learning

By : Kamath, Krishna Choppella
3.4 (9)
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Mastering Java Machine Learning

Mastering Java Machine Learning

3.4 (9)
By: 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 (13 chapters)
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10
A. Linear Algebra
12
Index

Machine learning applications

Given the rapidly growing use of machine learning in diverse areas of human endeavor, any attempt to list typical applications in the different industries where some form of machine learning is in use must necessarily be incomplete. Nevertheless, in this section, we list a broad set of machine learning applications by domain and the type of learning employed:

Domain/Industry

Applications

Machine Learning Type

Financial

Credit risk scoring, fraud detection, and anti-money laundering

Supervised, unsupervised, graph models, time series, and stream learning

Web

Online campaigns, health monitoring, and ad targeting

Supervised, unsupervised, semi-supervised

Healthcare

Evidence-based medicine, epidemiological surveillance, drug events prediction, and claim fraud detection

Supervised, unsupervised, graph models, time series, and stream learning

Internet of things (IoT)

Cyber security, smart roads, and sensor health monitoring

Supervised, unsupervised, semi-supervised, and stream learning

Environment

Weather forecasting, pollution modeling, and water quality measurement

Time series, supervised, unsupervised, semi-supervised, and stream learning

Retail

Inventory, customer management and recommendations, layout, and forecasting

Time series, supervised, unsupervised, semi-supervised, and stream learning

Applications of machine learning

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