Withanawasam Apache Mahout Essentials

Implement top-notch machine learning algorithms for classification, clustering, and recommendations with Apache Mahout
1. Auflage 2025
ISBN: 978-1-78355-500-0
Verlag: De Gruyter
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 164 Seiten

Key FeaturesBook DescriptionIf 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 realtime 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 realworld practical scenarios
- Implement and evaluate widelyused 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 realtime use cases and use userbased and itembased recommendation algorithms
Who this book is for

Autoren/Hrsg.

Withanawasam, Jayani

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen

Über Autor(innen)

Withanawasam Jayani :

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.