Mastering Java for Data Science: Analytics and more for production-ready applications - Couverture souple

Grigorev, Alexey

Couverture souple

ISBN 10 : 1782174273 ISBN 13 : 9781782174271

Editeur : Packt Publishing, 2017

Synopsis

Key Features

• An overview of modern Data Science and Machine Learning libraries available in Java
• Coverage of a broad set of topics, going from the basics of Machine Learning to Deep Learning and Big Data frameworks.
• Easy-to-follow illustrations and the running example of building a search engine.

Book Description

Java is the most popular programming language, according to the TIOBE index, and it is a typical choice for running production systems in many companies, both in the startup world and among large enterprises.

Not surprisingly, it is also a common choice for creating data science applications: it is fast and has a great set of data processing tools, both built-in and external. What is more, choosing Java for data science allows you to easily integrate solutions with existing software, and bring data science into production with less effort.

This book will teach you how to create data science applications with Java. First, we will revise the most important things when starting a data science application, and then brush up the basics of Java and machine learning before diving into more advanced topics. We start by going over the existing libraries for data processing and libraries with machine learning algorithms. After that, we cover topics such as classification and regression, dimensionality reduction and clustering, information retrieval and natural language processing, and deep learning and big data.

Finally, we finish the book by talking about the ways to deploy the model and evaluate it in production settings.

What you will learn

• Get a solid understanding of the data processing toolbox available in Java
• Explore the data science ecosystem available in Java

A propos du livre

Présentation de l'éditeur

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

Présentation de l'éditeur

Key Features

• An overview of modern Data Science and Machine Learning libraries available in Java
• Coverage of a broad set of topics, going from the basics of Machine Learning to Deep Learning and Big Data frameworks.
• Easy-to-follow illustrations and the running example of building a search engine.

Book Description

Java is the most popular programming language, according to the TIOBE index, and it is a typical choice for running production systems in many companies, both in the startup world and among large enterprises.

Not surprisingly, it is also a common choice for creating data science applications: it is fast and has a great set of data processing tools, both built-in and external. What is more, choosing Java for data science allows you to easily integrate solutions with existing software, and bring data science into production with less effort.

This book will teach you how to create data science applications with Java. First, we will revise the most important things when starting a data science application, and then brush up the basics of Java and machine learning before diving into more advanced topics. We start by going over the existing libraries for data processing and libraries with machine learning algorithms. After that, we cover topics such as classification and regression, dimensionality reduction and clustering, information retrieval and natural language processing, and deep learning and big data.

Finally, we finish the book by talking about the ways to deploy the model and evaluate it in production settings.

What you will learn

• Get a solid understanding of the data processing toolbox available in Java
• Explore the data science ecosystem available in Java

Détails de l'édition

• Éditeur: Packt Publishing
• Date d'édition: 2017
• Langue: anglais
• ISBN 10: 1782174273
• ISBN 13: 9781782174271
• Reliure: Broché
• Nombre de pages: 364
• Coordonnées du fabricant: non disponible
• Personne responsable: non disponible