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Mastering Java Machine Learning

You're reading from   Mastering Java Machine Learning A Java developer's guide to implementing machine learning and big data architectures

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781785880513
Length 556 pages
Edition 1st Edition
Languages
Concepts
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Authors (2):
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Uday Kamath Uday Kamath
Author Profile Icon Uday Kamath
Uday Kamath
Krishna Choppella Krishna Choppella
Author Profile Icon Krishna Choppella
Krishna Choppella
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Toc

Table of Contents (13) Chapters Close

Preface 1. Machine Learning Review 2. Practical Approach to Real-World Supervised Learning FREE CHAPTER 3. Unsupervised Machine Learning Techniques 4. Semi-Supervised and Active Learning 5. Real-Time Stream Machine Learning 6. Probabilistic Graph Modeling 7. Deep Learning 8. Text Mining and Natural Language Processing 9. Big Data Machine Learning – The Final Frontier A. Linear Algebra B. Probability 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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