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Machine Learning in Java
Machine Learning in Java

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

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Profile Icon Ashish Bhatia Profile Icon Bostjan Kaluza Profile Icon Bostjan Kaluza
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Paperback Nov 2018 300 pages 2nd Edition
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Arrow left icon
Profile Icon Ashish Bhatia Profile Icon Bostjan Kaluza Profile Icon Bostjan Kaluza
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€32.99
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (1 Ratings)
Paperback Nov 2018 300 pages 2nd Edition
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Machine Learning in Java

Java Libraries and Platforms for Machine Learning

Implementing machine learning algorithms by yourself is probably the best way to learn machine learning, but you can progress much faster if you step on the shoulders of the giants and leverage one of the existing open source libraries.

This chapter reviews various libraries and platforms for machine learning in Java. The goal is to understand what each library brings to the table and what kind of problems it is able to solve.

In this chapter, we will cover the following topics:

  • The requirement of Java for implementing a machine learning application
  • Weka, a general purpose machine learning platform
  • The Java machine learning library, a collection of machine learning algorithms
  • Apache Mahout, a scalable machine learning platform
  • Apache Spark, a distributed machine learning library
  • Deeplearning4j, a deep learning library
  • MALLET,...

The need for Java

New machine learning algorithms are often first scripted at university labs, gluing together several languages such as shell scripting, Python, R, MATLAB, Scala, or C++ to provide a new concept and theoretically analyze its properties. An algorithm might take a long path of refactoring before it lands in a library with standardized input or output and interfaces. While Python, R, and MATLAB are quite popular, they are mainly used for scripting, research, and experimenting. Java, on the other hand, is the de facto enterprise language, which could be attributed to static typing, robust IDE support, good maintainability, as well as decent threading model and high performance concurrent data structure libraries. Moreover, there are already many Java libraries available for machine learning, which makes it really convenient to apply them in existing Java applications...

Machine learning libraries

There are over 70 Java-based open source machine learning projects listed on the MLOSS.org website, and probably many more unlisted projects live at university servers, GitHub, or Bitbucket. In this section, we will review the major libraries and platforms, the kind of problems they can solve, the algorithms they support, and the kind of data they can work with.

Weka

Waikato Environment for Knowledge Analysis (WEKA) is a machine learning library that was developed at the University of Waikato, New Zealand, and is probably the most well-known Java library. It is a general purpose library that is able to solve a wide variety of machine learning tasks, such as classification, regression, and clustering...

Building a machine learning application

Machine learning applications, especially those focused on classification, usually follow the same high-level workflow that's shown in the following diagram. The workflow is comprised of two phases—training the classifier and the classification of new instances. Both phases share common steps, as shown here:

First, we use a set of training data, select a representative subset as the training set, preprocess the missing data, and extract its features. A selected supervised learning algorithm is used to train a model, which is deployed in the second phase. The second phase puts a new data instance through the same preprocessing and feature extraction procedure and applies the learned model to obtain the instance label. If you are able to collect new labelled data, periodically rerun the learning phase to retrain the model and...

Summary

Selecting a machine learning library has an important impact on your application architecture. The key is to consider your project requirements. What kind of data do you have? What kind of problem are you trying to solve? Is your data big? Do you need distributed storage? What kind of algorithm are you planning to use? Once you figure out what you need to solve your problem, pick a library that best fits your needs.

In the next chapter, we will cover how to complete basic machine learning tasks such as classification, regression, and clustering by using some of the presented libraries.

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Key benefits

  • 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

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.

Who is this book 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.

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
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Table of Contents

12 Chapters
Applied Machine Learning Quick Start Chevron down icon Chevron up icon
Java Libraries and Platforms for Machine Learning Chevron down icon Chevron up icon
Basic Algorithms - Classification, Regression, and Clustering Chevron down icon Chevron up icon
Customer Relationship Prediction with Ensembles Chevron down icon Chevron up icon
Affinity Analysis Chevron down icon Chevron up icon
Recommendation Engines with Apache Mahout Chevron down icon Chevron up icon
Fraud and Anomaly Detection Chevron down icon Chevron up icon
Image Recognition with Deeplearning4j Chevron down icon Chevron up icon
Activity Recognition with Mobile Phone Sensors Chevron down icon Chevron up icon
Text Mining with Mallet - Topic Modeling and Spam Detection Chevron down icon Chevron up icon
What Is Next? Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

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I really liked it. Gud book for beginners.
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