Machine Learning with Java

Name: Machine Learning with Java
ISBN: 9787115466808

Bostjan KaluzaPosts & Telecom Press2017

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Chapters: 11
Series: Turing Programming Series
ISBN: 9787115466808

Summary

This book is an introductory guide to machine learning for Java developers. It first lays out the basic concepts of machine learning and data science, explaining when a problem is well suited to a machine learning solution and walking through the complete preparatory pipeline—from data collection and cleaning, missing value imputation, and outlier handling to feature transformation and reduction. It then surveys the machine learning libraries and platforms available in the Java ecosystem, including Weka, Apache Mahout, Apache Spark, Deeplearning4j, and MALLET, comparing the scenarios each is suited to. At the algorithmic level, the book covers foundational tasks such as classification, regression, and clustering, and also touches on application areas including ensemble methods, association analysis, recommendation, anomaly and fraud detection, image recognition, sensor-based activity recognition, and text mining. Each chapter centers on a specific task, presenting the approach, tool selection, and an example workflow; readers can consult chapters as needed or read straight through following the thread of data processing. At roughly 170 pages, the book moves at a brisk pace, focusing on getting readers up and running with Java machine learning libraries rather than deriving formulas. It is well suited as a first hands-on reference for readers with a Java background who are just entering the field of machine learning.

Editor's review

市面上讲机器学习的书大多以 Python 为载体，对长期写 Java、又不想在项目里多引入一套技术栈的开发者来说，往往只能“看懂了，但落不了地”。这本书补的正是这个缺口：它不追求算法推导的完整，而是把 Weka、Mahout、Spark、Deeplearning4j、MALLET 这些 Java 侧工具摆出来，告诉你面对分类、回归、聚类、推荐、异常检测、文本挖掘这些具体任务时，该挑哪个库、大致怎么走流程。全书只有一百七十多页，章节短小，每章围绕一个任务独立成篇，非常适合通勤或周末分次读完。语言偏工程化，示例驱动，读完能对 Java 机器学习的工具版图形成一张清晰的“地图”。需要提醒的是，书中涉及的库版本与今天已有距离，部分示例可能需要调整依赖才能跑通；另外如果你期待深入的数学推导或最新的深度学习方法，这本书并不是那个选择。

Who it's for

A good fit for

有 Java 基础、想用熟悉语言做数据分析和机器学习的开发者
需要快速了解 Weka、Mahout、Spark、MALLET 等 Java 机器学习库选型的工程师
在校学生或转行者，希望用一本薄书建立机器学习工程实践的整体印象
后端工程师，要在现有 Java 系统中加入推荐、异常检测或文本分类能力

Not a good fit for

想系统学习算法数学原理、需要推导过程的读者
完全没有 Java 基础、希望直接从零学编程的入门者
追求最新深度学习框架（如 PyTorch、TensorFlow 新版）实践经验的读者

Reading tips

先读第 1、2 章建立概念和工具地图，再按自己手上的任务挑选后续章节精读，不必按顺序通读。
动手前先确认书中各库的当前版本与依赖，示例代码可能需要小幅修改才能在本地跑通。
每读完一章，用自己领域的一小份真实数据复现流程，比照抄示例更有收获。
把本书当索引而非教程：遇到具体任务时回查对应章节的工具选型建议，再配合官方文档深入。

About the publisher

Posts & Telecom Press China · Founded in 1953

Posts & Telecom Press is known for its strengths in science and technology, computing, and information and communications titles, and has extended its publishing into photography, art, software learning, and inspirational growth, giving it a fairly diverse range. Its catalog includes computing and communications works such as Refactoring and Patterns (Revised Edition) and Huawei Switch Learning Guide (Second Edition), alongside bilingual collector's editions such as The Little Prince (Chinese-English-French illustrated collector's edition), The Great Gatsby, and Three Days to See. Beijing Ten Years: 1980-1990 and Ghost Blade: WLOP Personal Illustration Collection reflect its presence in photography and art. Its authors include Antoine de Saint-Exupéry, Helen Keller, Joshua Kerievsky, Wang Da, Zhang Zhaozeng, and WLOP.

Chapter 1 A Quick Start to Machine Learning Applications
Chapter 2 Java Libraries and Platforms for Machine Learning
Chapter 3 Basic Algorithms—Classification, Regression, and Clustering
Chapter 4 Predicting Customer Relationships with Ensemble Methods
Chapter 5 Association Analysis
Chapter 6 Building a Recommendation Engine with Apache Mahout
Chapter 7 Fraud and Anomaly Detection
Chapter 8 Image Recognition with Deeplearning4j
Chapter 9 Activity Recognition with Smartphone Sensors
Chapter 10 Text Mining with MALLET—Topic Models and Spam Detection
Chapter 11 Advanced Machine Learning

FAQ

这本书需要什么基础？

需要能读懂并编写 Java 代码，了解类、集合和基本构建工具即可；机器学习方面假定是初学者，概念从第 1 章讲起，不要求数学基础特别扎实。

书中主要讲哪些 Java 库？

据图灵社区公布的目录，涉及 Weka、Apache Mahout、Apache Spark、Deeplearning4j 和 MALLET，第 2 章专门比较了这些库的定位与适用场景。

和其他同名的《Java机器学习》是同一本吗？

不是。市面上另有乌黛·卡马特等人著、机械工业出版社 2018 年出版的《Java机器学习》（原书 Mastering Java Machine Learning），内容更深、篇幅更大；本书是卡鲁扎著、武传海译的人民邮电版。

书中的代码示例还能跑吗？

本书 2017 年出版，所涉及的库此后多有版本更迭，示例依赖和 API 可能需要调整；建议以思路和流程为主，具体调用参考各库当前官方文档。

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About the author

Bostjan Kaluza Author

Bostjan Kaluza is an author in the fields of computer science and artificial intelligence. Publicly available information about him is limited, and his nationality, dates of birth and death, and personal background are not clearly documented. As far as the library's holdings show, only one of his works has been translated into Chinese, namely Java Machine Learning. The book falls under the categories of computing, artificial intelligence, and programming design, and is aimed at readers who wish to carry out machine learning practice within the Java ecosystem. It emphasizes combining algorithmic principles with programming implementation and has strong technical practicality. In terms of writing style, the author tends to discuss concrete development tools and focuses on operability rather than offering a general introduction to theory. Such works usually serve engineers and learners with some programming background, and provide a useful reference for Java-oriented machine learning introduction and practice in the Chinese technical book market.