Java编程实战：打造机器学习应用

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"《Java中的机器学习》是一本专为编程爱好者特别是Java开发者设计的实用指南，旨在帮助读者利用Java的关键机器学习库设计、构建并部署自己的机器学习应用。这本书适合两种类型的读者：一是已经熟悉Java基础的程序员，他们可以借此深入了解如何将机器学习技术融入到Java项目中；二是对于Java和机器学习都感兴趣的初学者，它提供了一个从零开始学习的路径，通过一步步的手把手教学，让读者能够掌握基础算法及其在Java中的实现。作者Boštjan Kaluža以其深入浅出的方式讲解，确保读者能够在阅读过程中理解各种机器学习概念和技术。书中涵盖了机器学习的常用算法，包括但不限于监督学习、无监督学习、深度学习等，这些内容都是以Java代码的形式呈现，便于读者实践操作。此外，本书特别强调理论与实践的结合，适合那些希望自行实现机器学习算法的开发者。尽管《MachineLearninginJava》的出版时间是2016年，但其内容仍然具有较高的实用价值，因为机器学习领域的基础知识和核心库并未发生重大变化。全书享有版权保护，未经许可不得复制或传播，以保证知识的完整性和权威性。推荐指数为4.1星，表明该书获得了读者的高度评价，但也暗示可能存在一些改进的空间。《Java中的机器学习》是一本值得投资的资源，对于想要在Java环境中开展机器学习工作的专业人士来说，它能提供坚实的基础和实战经验，是提升技能、扩展职业领域的重要参考资料。无论你是经验丰富的Java开发者还是对机器学习感兴趣的新手，这本书都将是你探索这个交叉学科领域的理想伴侣。"

Table of Contents

[ vii ]

Machine learning in the cloud 215

Machine learning as a service 216

Web resources and competitions 217

Datasets 217

Online courses 218

Competitions 219

Websites and blogs 219

Venues and conferences 220

Summary 220

Appendix: References 221

Index 225

Preface

[ x ]

Chapter 3, Basic Algorithms – Classication, Regression, and Clustering, starts with basic

machine learning tasks, introducing the key algorithms for classication, regression,

and clustering, using small, easy-to-understand datasets.

Chapter 4, Customer Relationship Prediction with Ensembles, dives into a real-world

marketing database, where the task is to predict the customer that will churn, upsell,

and cross-sell. The problem is attacked with ensemble methods, following the steps

of KDD Cup-winning solution.

Chapter 5, Afnity Analysis, discusses how to analyze co-occurrence relationships

using association rule mining. We will look into market basket analysis to

understand the purchasing behavior of customers and discuss applications of the

approach to other domains.

Chapter 6, Recommendation Engine with Apache Mahout, explains the basic concepts

required to understand recommendation engine principles, followed by two

applications leveraging Apache Mahout to build content-based ltering and

collaborative recommender.

Chapter 7, Fraud and Anomaly Detection, introduces the background to anomalous and

suspicious pattern detection, followed by two practical applications on detecting

frauds in insurance claims and detecting anomalies in website trafc.

Chapter 8, Image Recognition with Deeplearning4j, introduces image recognition and

reviews fundamental neural network architectures. We will then discuss how

to implement various deep learning architectures with deeplearning4j library to

recognize handwritten digits.

Chapter 9, Activity Recognition with Mobile Phone Sensors, tackles the problem

of recognizing patterns from sensor data. This chapter introduces the activity

recognition process, explains how to collect data with an Android device, and

presents a classication model to recognize activities of daily living.

Chapter 10, Text Mining with Mallet – Topic Modeling and Spam Detection, explains the

basics of text mining, introduces the text processing pipeline, and shows how to

apply this to two real-world problems: topic modeling and document classication.

Chapter 11, What is Next?, concludes the book with practical advice about how

to deploy models and gives you further pointers about where to nd additional

resources, materials, venues, and technologies to dive deeper into machine learning.

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