Machine Learning in Java

Design, build, and deploy your own machine learning applications by leveraging key Java machine learning libraries
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Machine Learning in Java

Bostjan Kaluza

4 customer reviews
Design, build, and deploy your own machine learning applications by leveraging key Java machine learning libraries
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Book Details

ISBN 139781784396589
Paperback258 pages

Book Description

As the amount of data 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 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.

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.

Table of Contents

Chapter 1: Applied Machine Learning Quick Start
Machine learning and data science
Data and problem definition
Data collection
Data pre-processing
Unsupervised learning
Supervised learning
Generalization and evaluation
Summary
Chapter 2: Java Libraries and Platforms for Machine Learning
The need for Java
Machine learning libraries
Building a machine learning application
Summary
Chapter 3: Basic Algorithms – Classification, Regression, and Clustering
Before you start
Classification
Regression
Clustering
Summary
Chapter 4: Customer Relationship Prediction with Ensembles
Customer relationship database
Basic naive Bayes classifier baseline
Basic modeling
Advanced modeling with ensembles
Summary
Chapter 5: Affinity Analysis
Market basket analysis
Association rule learning
The supermarket dataset
Discover patterns
Other applications in various areas
Summary
Chapter 6: Recommendation Engine with Apache Mahout
Basic concepts
Getting Apache Mahout
Building a recommendation engine
Content-based filtering
Summary
Chapter 7: Fraud and Anomaly Detection
Suspicious and anomalous behavior detection
Suspicious pattern detection
Anomalous pattern detection
Fraud detection of insurance claims
Anomaly detection in website traffic
Summary
Chapter 8: Image Recognition with Deeplearning4j
Introducing image recognition
Image classification
Summary
Chapter 9: Activity Recognition with Mobile Phone Sensors
Introducing activity recognition
Collecting data from a mobile phone
Building a classifier
Summary
Chapter 10: Text Mining with Mallet – Topic Modeling and Spam Detection
Introducing text mining
Installing Mallet
Working with text data
Topic modeling for BBC news
E-mail spam detection
Summary
Chapter 11: What is Next?
Machine learning in real life
Standards and markup languages
Machine learning in the cloud
Web resources and competitions
Summary

What You Will Learn

  • Understand the basic steps of applied machine learning and how to differentiate among various machine learning approaches
  • Discover key Java machine learning libraries, what each library brings to the table, and what kind of problems each are able to solve
  • Learn how to implement classification, regression, and clustering
  • Develop a sustainable strategy for customer retention 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, algorithms, and the toolbox for deep learning
  • Write your own activity recognition model for eHealth applications using mobile sensors

Authors

Table of Contents

Chapter 1: Applied Machine Learning Quick Start
Machine learning and data science
Data and problem definition
Data collection
Data pre-processing
Unsupervised learning
Supervised learning
Generalization and evaluation
Summary
Chapter 2: Java Libraries and Platforms for Machine Learning
The need for Java
Machine learning libraries
Building a machine learning application
Summary
Chapter 3: Basic Algorithms – Classification, Regression, and Clustering
Before you start
Classification
Regression
Clustering
Summary
Chapter 4: Customer Relationship Prediction with Ensembles
Customer relationship database
Basic naive Bayes classifier baseline
Basic modeling
Advanced modeling with ensembles
Summary
Chapter 5: Affinity Analysis
Market basket analysis
Association rule learning
The supermarket dataset
Discover patterns
Other applications in various areas
Summary
Chapter 6: Recommendation Engine with Apache Mahout
Basic concepts
Getting Apache Mahout
Building a recommendation engine
Content-based filtering
Summary
Chapter 7: Fraud and Anomaly Detection
Suspicious and anomalous behavior detection
Suspicious pattern detection
Anomalous pattern detection
Fraud detection of insurance claims
Anomaly detection in website traffic
Summary
Chapter 8: Image Recognition with Deeplearning4j
Introducing image recognition
Image classification
Summary
Chapter 9: Activity Recognition with Mobile Phone Sensors
Introducing activity recognition
Collecting data from a mobile phone
Building a classifier
Summary
Chapter 10: Text Mining with Mallet – Topic Modeling and Spam Detection
Introducing text mining
Installing Mallet
Working with text data
Topic modeling for BBC news
E-mail spam detection
Summary
Chapter 11: What is Next?
Machine learning in real life
Standards and markup languages
Machine learning in the cloud
Web resources and competitions
Summary

Book Details

ISBN 139781784396589
Paperback258 pages
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