Book Image

Machine Learning in Java - Second Edition

By : AshishSingh Bhatia, Bostjan Kaluza
Book Image

Machine Learning in Java - Second Edition

By: AshishSingh 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)

Standards and markup languages

As predictive models become more pervasive, the need for sharing the models and completing the modeling process leads to formalization of development process and interchangeable formats. In this section, we'll review two de facto standards, one covering data science processes and the other specifying an interchangeable format for sharing models between applications.

CRISP-DM

Cross Industry Standard Process for Data Mining (CRISP-DM) describes a data-mining process commonly used by data scientists in industry. CRISP-DM breaks the data-mining science process into six major phases:

  • Business understanding
  • Data understanding
  • Data preparation
  • Modeling
  • Evaluation
  • Deployment

In the following diagram...