Machine Learning in Java

Design, build, and deploy your own machine learning applications by leveraging key Java machine learning librariesAbout This Book Develop a sound strategy to solve predictive modelling problems using the most popular machine learning Java libraries Explore a broad variety of data processing, machine learning, and natural language processing through diagrams, source code, and real-world applications Packed with practical advice and tips to help you get to grips with applied machine learningWho This Book Is ForIf 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 in real life. You should be familiar with Java programming and data mining concepts to make the most of this book, but no prior experience with data mining packages is necessary. 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 sensorsIn DetailAs 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. Style and approachThis is a practical tutorial that uses hands-on examples to step through some real-world applications of machine learning. Without shying away from the technical details, you will explore machine learning with Java libraries using clear and practical examples. You will explore how to prepare data for analysis, choose a machine learning method, and measure the success of the process.

Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Applied Machine Learning Quick Start; Machine learning and data science; What kind of problems can machine learning solve?; Applied Machine Learning workflow; Data and problem definition; Measurement scales; Data collection; Find or observe data; Generate data; Sampling traps; Data pre-processing; Data cleaning; Fill missing values; Remove outliers; Data transformation; Data reduction; Unsupervised learning; Find similar items; Euclidean distances.

Non-Euclidean distancesThe curse of dimensionality; Clustering; Supervised learning; Classification; Decision tree learning; Probabilistic classifiers; Kernel methods; Artificial neural networks; Ensemble learning; Evaluating classification; Regression; Linear regression; Evaluating regression; Generalization and evaluation; Underfitting and overfitting; Train and test sets; Cross-validation; Leave-one-out validation; Stratification; Summary; Chapter 2: Java Libraries and Platforms for Machine Learning; The need for Java; Machine learning libraries; Weka; Java machine learning; Apache Mahout.

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