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Machine Learning in Java - Second Edition

By : Ashish Bhatia, Bostjan Kaluza

5 (1)

Buy this Book

Machine Learning in Java

5 (1)

By: Ashish Bhatia, Bostjan Kaluza

Buy this Book

Overview of this book

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.

Preface

Who this book is for

What this book covers

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Free Chapter

Applied Machine Learning Quick Start

Machine learning and data science

Data and problem definition

Data collection

Data preprocessing

Unsupervised learning

Supervised learning

Generalization and evaluation

Summary

Java Libraries and Platforms for Machine Learning

The need for Java

Machine learning libraries

Building a machine learning application

Summary

Basic Algorithms - Classification, Regression, and Clustering

Before you start

Classification

Regression

Clustering

Summary

Customer Relationship Prediction with Ensembles

The customer relationship database

Basic Naive Bayes classifier baseline

Basic modeling

Advanced modeling with ensembles

Summary

Affinity Analysis

Market basket analysis

Association rule learning

The supermarket dataset

Discover patterns

Other applications in various areas

Summary

Recommendation Engines with Apache Mahout

Basic concepts

Getting Apache Mahout

Building a recommendation engine

Content-based filtering

Summary

Fraud and Anomaly Detection

Suspicious and anomalous behavior detection

Suspicious pattern detection

Anomalous pattern detection

Outlier detection using ELKI

Fraud detection in insurance claims

Anomaly detection in website traffic

Summary

Image Recognition with Deeplearning4j

Introducing image recognition

Image classification

Summary

Activity Recognition with Mobile Phone Sensors

Introducing activity recognition

Collecting data from a mobile phone

Building a classifier

Summary

Text Mining with Mallet - Topic Modeling and Spam Detection

Introducing text mining

Installing Mallet

Working with text data

Topic modeling for BBC News

Detecting email spam

Summary

What Is Next?

Machine learning in real life

Standards and markup languages

Machine learning in the cloud

Web resources and competitions

Summary

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Activity Recognition with Mobile Phone Sensors

While the previous chapter focused on pattern recognition in images, this chapter is all about recognizing patterns in sensor data, which, in contrast to images, has temporal dependencies. We will discuss how to recognize granular daily activities such as walking, sitting, and running using mobile phone inertial sensors. The chapter also provides references to related research and emphasizes best practices in the activity recognition community.

The topics covered in this chapter will include the following:

Introducing activity recognition, covering mobile phone sensors and the activity recognition pipeline
Collecting sensor data from mobile devices
Discussing activity classification and model evaluation
Deploying an activity recognition model