Mastering Java for Data Science

Alexey Grigorev

  • 出版商: Packt Publishing
  • 出版日期: 2017-04-28
  • 售價: $1,600
  • 貴賓價: 9.5$1,520
  • 語言: 英文
  • 頁數: 364
  • 裝訂: Paperback
  • ISBN: 1782174273
  • ISBN-13: 9781782174271
  • 相關分類: Java 程式語言資料科學
  • 立即出貨 (庫存 < 3)




Key Features

  • This comprehensive book shows you exactly how you can take your Java data science applications to production seamlessly
  • Dive deep into analytics, supervised and unsupervised learning, and much more with ease
  • Explore Java's various libraries to efficiently build and deploy data applications for the enterprise

Book Description

Java is the language of choice if you want to bring data science to production, thanks to its stability and rich set of libraries. Major big data solutions including Hadoop are written in Java. This book will teach you how to perform data analysis on big data in a much more sophisticated manner. If you are willing to take your data products to enterprise without changing your stack, this book will tell you how to do it with ease.

This book will quickly brush up on what you already know about using Java in data science applications and will then dive quickly into the advanced concepts to implement data science in production. The book covers topics such as advanced data science algorithms, preparing tricky data, advanced clustering, regression, classification, prediction, machine learning, and more.

We'll teach you how data science can be used effectively to analyze unstructured data and big data. This book will enable you to tackle the problems of advanced visualization, advanced statistics, scaling data science applications, deploying these applications in production, and many more. You will also learn about natural language processing, real-time analytics, deep learning, and neural networks.

What you will learn

  • Get a solid understanding of the data processing toolbox available in Java
  • Explore the data science ecosystem available in Java and other JVM languages
  • Understand when to use Java and what is best to do outside of Java
  • Deal with the machine learning task at hand and bring the results directly to production
  • Get state-of-the-art performance with xgboost and deeplearning4j
  • Build applications that scale and process large amounts of data in real time