Supporting material (code, libs etc) for Machine Learning in Java (Packtpub Publishing).

Machine Learning in Java will provide you with the techniques and tools you need to quickly gain insight from complex data. 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. 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 explore related web resources and technologies that will help you take your learning to the next level.

Code is organized by chapters - each chapter is a seperate Eclipse project with all the corresponding libraries and datasets.

1. Applied Machine Learning Quick Start
2. Java Tools and Libraries for Machine Learning
3. Basic Algorithms: Classification, Regression, and Clustering
4. Customer Relationship Prediction
5. Affinity Analysis
6. Recommendation Engine with Apache Mahout
7. Fraud and Anomaly Detection
8. Image Recognition with DeepLearning4java
9. Activity Recognition with Mobile Phone Sensors
10. Text Mining with Mallet: Topic Modelling and Spam Detection
11. What is Next?

[Sample chapters] (http://machine-learning-in-java.com) and additional materials are available.

• [Python Machine Learning] (https://www.packtpub.com/big-data-and-business-intelligence/python-machine-learning?utm_source=github&utm_medium=repository&utm_campaign=9781783555130)
• [R Machine Learning By Example] (https://www.packtpub.com/big-data-and-business-intelligence/r-machine-learning-example?utm_source=github&utm_medium=repository&utm_campaign=9781784390846)