Machine Learning in Java

Machine Learning in Java
Machine Learning in Java by Boštjan Kaluža
English | 2016 | ISBN: 1784396589 | 258 Pages | EPUB, AZW3, PDF (conv) | 15 MB

Design, build, and deploy your own machine learning applications by leveraging key Java machine learning libraries
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.
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