Machine Learning in Java: Helpful techniques to design, build, and deploy powerful machine learning applications in Java, 2nd Edition

Leverage the power of Java and its associated machine learning libraries to build powerful predictive models

Key Features

• Solve predictive modeling problems using the most popular machine learning Java libraries
• Explore data processing, machine learning, and NLP concepts using JavaML, WEKA, MALLET libraries
• Practical examples, tips, and tricks to help you understand applied machine learning in Java

Book Description

As the amount of data in the world continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of big data and Data Science. The main challenge is how to transform data into actionable knowledge.

Machine Learning in Java will provide you with the techniques and tools you need. You will start by learning how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. The code in this book works for JDK 8 and above, the code is tested on JDK 11.

Moving on, you will discover how to detect anomalies and fraud, and ways to perform activity recognition, image recognition, and text analysis. By the end of the book, you will have explored related web resources and technologies that will help you take your learning to the next level.

By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data.

What you will learn

• Discover key Java machine learning libraries
• Implement concepts such as classification, regression, and clustering
• Develop a customer retention strategy by predicting likely churn candidates
• Build a scalable recommendation engine with Apache Mahout
• Apply machine learning to fraud, anomaly, and outlier detection
• Experiment with deep learning concepts and algorithms
• Write your own activity recognition model for eHealth applications

Who this book is for

If you want to learn how to use Java's machine learning libraries to gain insight from your data, this book is for you. It will get you up and running quickly and provide you with the skills you need to successfully create, customize, and deploy machine learning applications with ease. You should be familiar with Java programming and some basic data mining concepts to make the most of this book, but no prior experience with machine learning is required.

Table of Contents

1. Applied Machine Learning Quick Start
2. Java Libraries and Platforms for Machine Learning
3. Basic Algorithms – Classification, Regression, and Clustering
4. Customer Relationship Prediction with Ensembles
5. Affinity Analysis
6. Recommendation Engine with Apache Mahout
7. Fraud and Anomaly Detection
8. Image Recognition with Deeplearning4j
9. Activity Recognition with Mobile Phone Sensors
10. Text Mining with Mallet – Topic Modeling and Spam Detection
11. What is Next?

利用Java及其相關的機器學習庫來建立強大的預測模型

主要特點:
- 使用最流行的Java機器學習庫解決預測建模問題
- 使用JavaML、WEKA和MALLET庫探索數據處理、機器學習和NLP概念
- 提供實用示例、技巧和訣竅，幫助您理解Java中應用機器學習的方法

書籍描述:
隨著世界上的數據量以幾乎無法理解的速度增長，能夠理解和處理數據正在成為競爭組織的關鍵區別因素。機器學習應用無處不在，從自動駕駛汽車、垃圾郵件檢測、文件搜索和交易策略到語音識別。這使得機器學習非常適合當今大數據和數據科學時代。主要挑戰在於如何將數據轉化為可行的知識。

《Java機器學習》將為您提供所需的技術和工具。您將首先學習如何將機器學習方法應用於各種常見任務，包括分類、預測、預測、市場籃分析和聚類。本書中的代碼適用於JDK 8及以上版本，已在JDK 11上進行測試。

接下來，您將了解如何檢測異常和欺詐，以及執行活動識別、圖像識別和文本分析的方法。通過閱讀本書，您將探索相關的網絡資源和技術，幫助您提升學習水平。

通過將最有效的機器學習方法應用於實際問題，您將獲得實踐經驗，從而改變您對數據的思考方式。

您將學到什麼:
- 探索關鍵的Java機器學習庫
- 實現分類、回歸和聚類等概念
- 通過預測可能的流失候選人來制定客戶保留策略
- 使用Apache Mahout構建可擴展的推薦引擎
- 將機器學習應用於欺詐、異常和離群值檢測
- 實驗深度學習概念和算法
- 為電子健康應用程序編寫自己的活動識別模型

本書適合對如何使用Java的機器學習庫從數據中獲取洞察力感興趣的讀者。它將使您能夠快速上手，並提供您成功創建、自定義和部署機器學習應用程序所需的技能。您應該熟悉Java編程和一些基本的數據挖掘概念，但不需要機器學習的先前經驗。

目錄:
1. 應用機器學習快速入門
2. Java機器學習庫和平台
3. 基本算法-分類、回歸和聚類
4. 使用集成預測客戶關係
5. 關聯分析
6. 使用Apache Mahout構建推薦引擎
7. 欺詐和異常檢測
8. 使用Deeplearning4j進行圖像識別
9. 使用手機傳感器進行活動識別
10. 使用Mallet進行文本挖掘-主題建模和垃圾郵件檢測
11. 下一步是什麼?