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  • Book Overview & Buying Machine Learning in Java
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Machine Learning in Java

Machine Learning in Java - Second Edition

By : Ashish Bhatia, Bostjan Kaluza
5 (1)
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Machine Learning in Java

Machine Learning in Java

5 (1)
By: Ashish Bhatia, Bostjan Kaluza

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.
Table of Contents (13 chapters)
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Classification

We will start with the most commonly used machine learning technique: classification. As we reviewed in the first chapter, the main idea is to automatically build a mapping between the input variables and the outcome. In the following sections, we will look at how to load the data, select features, implement a basic classifier in Weka, and evaluate its performance.

Data

For this task, we will take a look at the ZOO database. The database contains 101 data entries of animals described with 18 attributes, as shown in the following table:

animal

aquatic

fins

hair

predator

legs

feathers

toothed

tail

eggs

backbone

domestic

milk

breathes

cat size

airborne

venomous

type...

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Programming languages
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Machine Learning in Java
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