Machine Learning in Java: Helpful techniques to design, build, and deploy powerful machine learning applications in Java, 2nd Edition

Machine Learning in Java: Helpful techniques to design, build, and deploy powerful machine learning applications in Java, 2nd Edition
Machine Learning in Java: Helpful techniques to design, build, and deploy powerful machine learning applications in Java, 2nd Edition by AshishSingh Bhatia
English | 2018 | ISBN: 1788474399 | 300 Pages | EPUB | 10 MB

Leverage the power of Java and its associated machine learning libraries to build powerful predictive models
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.
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