Apache Mahout Essentials - Couverture souple

Withanawasam, Jayani

Couverture souple

ISBN 10 : 1783554991 ISBN 13 : 9781783554997

Editeur : Packt Publishing, 2015

Synopsis

Implement top-notch machine learning algorithms for classification, clustering, and recommendations with Apache Mahout

About This Book

• Apply machine learning algorithms effectively in production environments with Apache Mahout
• Gain better insights into large, complex, and scalable datasets
• Fast-paced tutorial, covering the core concepts of Apache Mahout to implement machine learning on Big Data

Who This Book Is For

If you are a Java developer or data scientist, haven't worked with Apache Mahout before, and want to get up to speed on implementing machine learning on big data, then this is the perfect guide for you.

What You Will Learn

• Get started with the fundamentals of Big Data, batch, and real-time data processing with an introduction to Mahout and its applications
• Understand the key machine learning concepts behind algorithms in Apache Mahout
• Apply machine learning algorithms provided by Apache Mahout in real-world practical scenarios
• Implement and evaluate widely-used clustering, classification, and recommendation algorithms using Apache Mahout
• Discover tips and tricks to improve the accuracy and performance of your results
• Set up Apache Mahout in a production environment with Apache Hadoop
• Glance at the Spark DSL advancements in Apache Mahout 1.0
• Provide dynamic and interactive data visualizations for Apache Mahout
• Build a recommendation engine for real-time use cases and use user-based and item-based recommendation algorithms

In Detail

Apache Mahout is a scalable machine learning library with algorithms for clustering, classification, and recommendations. It empowers users to analyze patterns in large, diverse, and complex datasets faster and more scalably.

This book is an all-inclusive guide to analyzing large and complex datasets using Apache Mahout. It explains complicated but very effective machine learning algorithms simply, in relation to real-world practical examples.

Starting from the fundamental concepts of machine learning and Apache Mahout, this book guides you through Apache Mahout's implementations of machine learning techniques including classification, clustering, and recommendations. During this exciting walkthrough, real-world applications, a diverse range of popular algorithms and their implementations, code examples, evaluation strategies, and best practices are given for each technique. Finally, you will learn vdata visualization techniques for Apache Mahout to bring your data to life.

A propos du livre

Présentation de l'éditeur

Implement top-notch machine learning algorithms for classification, clustering, and recommendations with Apache Mahout

About This Book

• Apply machine learning algorithms effectively in production environments with Apache Mahout
• Gain better insights into large, complex, and scalable datasets
• Fast-paced tutorial, covering the core concepts of Apache Mahout to implement machine learning on Big Data

Who This Book Is For

If you are a Java developer or data scientist, haven't worked with Apache Mahout before, and want to get up to speed on implementing machine learning on big data, then this is the perfect guide for you.

What You Will Learn

• Get started with the fundamentals of Big Data, batch, and real-time data processing with an introduction to Mahout and its applications
• Understand the key machine learning concepts behind algorithms in Apache Mahout
• Apply machine learning algorithms provided by Apache Mahout in real-world practical scenarios
• Implement and evaluate widely-used clustering, classification, and recommendation algorithms using Apache Mahout
• Discover tips and tricks to improve the accuracy and performance of your results
• Set up Apache Mahout in a production environment with Apache Hadoop
• Glance at the Spark DSL advancements in Apache Mahout 1.0
• Provide dynamic and interactive data visualizations for Apache Mahout
• Build a recommendation engine for real-time use cases and use user-based and item-based recommendation algorithms

In Detail

Apache Mahout is a scalable machine learning library with algorithms for clustering, classification, and recommendations. It empowers users to analyze patterns in large, diverse, and complex datasets faster and more scalably.

This book is an all-inclusive guide to analyzing large and complex datasets using Apache Mahout. It explains complicated but very effective machine learning algorithms simply, in relation to real-world practical examples.

Starting from the fundamental concepts of machine learning and Apache Mahout, this book guides you through Apache Mahout's implementations of machine learning techniques including classification, clustering, and recommendations. During this exciting walkthrough, real-world applications, a diverse range of popular algorithms and their implementations, code examples, evaluation strategies, and best practices are given for each technique. Finally, you will learn vdata visualization techniques for Apache Mahout to bring your data to life.

Biographie de l'auteur

Jayani Withanawasam

Jayani Withanawasam is R&D engineer and a senior software engineer at Zaizi Asia, where she focuses on applying machine learning techniques to provide smart content management solutions. She is currently pursuing an MSc degree in artificial intelligence at the University of Moratuwa, Sri Lanka, and has completed her BE in software engineering (with first class honors) from the University of Westminster, UK. She has more than 6 years of industry experience, and she has worked in areas such as machine learning, natural language processing, and semantic web technologies during her tenure. She is passionate about working with semantic technologies and big data.

Détails de l'édition

• Éditeur: Packt Publishing
• Date d'édition: 2015
• Langue: anglais
• ISBN 10: 1783554991
• ISBN 13: 9781783554997
• Reliure: Broché
• Nombre de pages: 164
• Coordonnées du fabricant: non disponible
• Personne responsable: non disponible