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Apache Mahout
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Download or read book Apache Mahout written by Dmitriy Lyubimov and published by . This book was released on 2016-02-18 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: Apache Mahout: Beyond MapReduce. Distributed algorithm design This book is about designing mathematical and Machine Learning algorithms using the Apache Mahout "Samsara" platform. The material takes on best programming practices as well as conceptual approaches to attacking Machine Learning problems in big datasets. Math is explained, followed by code examples of distributed and in-memory computations. Written by Apache Mahout committers for people who want to learn how to design distributed math algorithms as well as how to use some of the new Mahout "Samsara" algorithms off-the-shelf. The book covers Apache Mahout 0.10 and 0.11.
Download or read book Mahout in Action written by Sean Owen and published by Simon and Schuster. This book was released on 2011-10-04 with total page 616 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Mahout in Action is a hands-on introduction to machine learning with Apache Mahout. Following real-world examples, the book presents practical use cases and then illustrates how Mahout can be applied to solve them. Includes a free audio- and video-enhanced ebook. About the Technology A computer system that learns and adapts as it collects data can be really powerful. Mahout, Apache's open source machine learning project, captures the core algorithms of recommendation systems, classification, and clustering in ready-to-use, scalable libraries. With Mahout, you can immediately apply to your own projects the machine learning techniques that drive Amazon, Netflix, and others. About this Book This book covers machine learning using Apache Mahout. Based on experience with real-world applications, it introduces practical use cases and illustrates how Mahout can be applied to solve them. It places particular focus on issues of scalability and how to apply these techniques against large data sets using the Apache Hadoop framework. This book is written for developers familiar with Java -- no prior experience with Mahout is assumed. Owners of a Manning pBook purchased anywhere in the world can download a free eBook from manning.com at any time. They can do so multiple times and in any or all formats available (PDF, ePub or Kindle). To do so, customers must register their printed copy on Manning's site by creating a user account and then following instructions printed on the pBook registration insert at the front of the book. What's Inside Use group data to make individual recommendations Find logical clusters within your data Filter and refine with on-the-fly classification Free audio and video extras Table of Contents Meet Apache Mahout PART 1 RECOMMENDATIONS Introducing recommenders Representing recommender data Making recommendations Taking recommenders to production Distributing recommendation computations PART 2 CLUSTERING Introduction to clustering Representing data Clustering algorithms in Mahout Evaluating and improving clustering quality Taking clustering to production Real-world applications of clustering PART 3 CLASSIFICATION Introduction to classification Training a classifier Evaluating and tuning a classifier Deploying a classifier Case study: Shop It To Me
Book Synopsis Apache Mahout Essentials by : Jayani Withanawasam
Download or read book Apache Mahout Essentials written by Jayani Withanawasam and published by Packt Publishing Ltd. This book was released on 2015-06-19 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Apache Mahout Clustering Designs by : Ashish Gupta
Download or read book Apache Mahout Clustering Designs written by Ashish Gupta and published by Packt Publishing Ltd. This book was released on 2015-10-08 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore clustering algorithms used with Apache Mahout About This Book Use Mahout for clustering datasets and gain useful insights Explore the different clustering algorithms used in day-to-day work A practical guide to create and evaluate your own clustering models using real world data sets Who This Book Is For This book is for developers who want to try out clustering on large datasets using Mahout. It will also be useful for those users who don't have background in Mahout, but have knowledge of basic programming and are familiar with basics of machine learning and clustering. It will be helpful if you know about clustering techniques with some other tool. What You Will Learn Explore clustering algorithms and cluster evaluation techniques Learn different types of clustering and distance measuring techniques Perform clustering on your data using K-Means clustering Discover how canopy clustering is used as pre-process step for K-Means Use the Fuzzy K-Means algorithm in Apache Mahout Implement Streaming K-Means clustering in Mahout Learn Spectral K-Means clustering implementation of Mahout In Detail As more and more organizations are discovering the use of big data analytics, interest in platforms that provide storage, computation, and analytic capabilities has increased. Apache Mahout caters to this need and paves the way for the implementation of complex algorithms in the field of machine learning to better analyse your data and get useful insights into it. Starting with the introduction of clustering algorithms, this book provides an insight into Apache Mahout and different algorithms it uses for clustering data. It provides a general introduction of the algorithms, such as K-Means, Fuzzy K-Means, StreamingKMeans, and how to use Mahout to cluster your data using a particular algorithm. You will study the different types of clustering and learn how to use Apache Mahout with real world data sets to implement and evaluate your clusters. This book will discuss about cluster improvement and visualization using Mahout APIs and also explore model-based clustering and topic modelling using Dirichlet process. Finally, you will learn how to build and deploy a model for production use. Style and approach This book is a hand's-on guide with examples using real-world datasets. Each chapter begins by explaining the algorithm in detail and follows up with showing how to use mahout for that algorithm using example data-sets.
Book Synopsis Machine Learning and Big Data by : Uma N. Dulhare
Download or read book Machine Learning and Big Data written by Uma N. Dulhare and published by John Wiley & Sons. This book was released on 2020-09-01 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is intended for academic and industrial developers, exploring and developing applications in the area of big data and machine learning, including those that are solving technology requirements, evaluation of methodology advances and algorithm demonstrations. The intent of this book is to provide awareness of algorithms used for machine learning and big data in the academic and professional community. The 17 chapters are divided into 5 sections: Theoretical Fundamentals; Big Data and Pattern Recognition; Machine Learning: Algorithms & Applications; Machine Learning's Next Frontier and Hands-On and Case Study. While it dwells on the foundations of machine learning and big data as a part of analytics, it also focuses on contemporary topics for research and development. In this regard, the book covers machine learning algorithms and their modern applications in developing automated systems. Subjects covered in detail include: Mathematical foundations of machine learning with various examples. An empirical study of supervised learning algorithms like Naïve Bayes, KNN and semi-supervised learning algorithms viz. S3VM, Graph-Based, Multiview. Precise study on unsupervised learning algorithms like GMM, K-mean clustering, Dritchlet process mixture model, X-means and Reinforcement learning algorithm with Q learning, R learning, TD learning, SARSA Learning, and so forth. Hands-on machine leaning open source tools viz. Apache Mahout, H2O. Case studies for readers to analyze the prescribed cases and present their solutions or interpretations with intrusion detection in MANETS using machine learning. Showcase on novel user-cases: Implications of Electronic Governance as well as Pragmatic Study of BD/ML technologies for agriculture, healthcare, social media, industry, banking, insurance and so on.
Book Synopsis Practical Hadoop Ecosystem by : Deepak Vohra
Download or read book Practical Hadoop Ecosystem written by Deepak Vohra and published by Apress. This book was released on 2016-09-30 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to use the Apache Hadoop projects, including MapReduce, HDFS, Apache Hive, Apache HBase, Apache Kafka, Apache Mahout, and Apache Solr. From setting up the environment to running sample applications each chapter in this book is a practical tutorial on using an Apache Hadoop ecosystem project. While several books on Apache Hadoop are available, most are based on the main projects, MapReduce and HDFS, and none discusses the other Apache Hadoop ecosystem projects and how they all work together as a cohesive big data development platform. What You Will Learn: Set up the environment in Linux for Hadoop projects using Cloudera Hadoop Distribution CDH 5 Run a MapReduce job Store data with Apache Hive, and Apache HBase Index data in HDFS with Apache Solr Develop a Kafka messaging system Stream Logs to HDFS with Apache Flume Transfer data from MySQL database to Hive, HDFS, and HBase with Sqoop Create a Hive table over Apache Solr Develop a Mahout User Recommender System Who This Book Is For: Apache Hadoop developers. Pre-requisite knowledge of Linux and some knowledge of Hadoop is required.
Download or read book Taming Text written by Grant Ingersoll and published by Simon and Schuster. This book was released on 2012-12-20 with total page 467 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Taming Text, winner of the 2013 Jolt Awards for Productivity, is a hands-on, example-driven guide to working with unstructured text in the context of real-world applications. This book explores how to automatically organize text using approaches such as full-text search, proper name recognition, clustering, tagging, information extraction, and summarization. The book guides you through examples illustrating each of these topics, as well as the foundations upon which they are built. About this Book There is so much text in our lives, we are practically drowningin it. Fortunately, there are innovative tools and techniquesfor managing unstructured information that can throw thesmart developer a much-needed lifeline. You'll find them in thisbook. Taming Text is a practical, example-driven guide to working withtext in real applications. This book introduces you to useful techniques like full-text search, proper name recognition,clustering, tagging, information extraction, and summarization.You'll explore real use cases as you systematically absorb thefoundations upon which they are built.Written in a clear and concise style, this book avoids jargon, explainingthe subject in terms you can understand without a backgroundin statistics or natural language processing. Examples arein Java, but the concepts can be applied in any language. Written for Java developers, the book requires no prior knowledge of GWT. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. Winner of 2013 Jolt Awards: The Best Books—one of five notable books every serious programmer should read. What's Inside When to use text-taming techniques Important open-source libraries like Solr and Mahout How to build text-processing applications About the Authors Grant Ingersoll is an engineer, speaker, and trainer, a Lucenecommitter, and a cofounder of the Mahout machine-learning project. Thomas Morton is the primary developer of OpenNLP and Maximum Entropy. Drew Farris is a technology consultant, software developer, and contributor to Mahout,Lucene, and Solr. "Takes the mystery out of verycomplex processes."—From the Foreword by Liz Liddy, Dean, iSchool, Syracuse University Table of Contents Getting started taming text Foundations of taming text Searching Fuzzy string matching Identifying people, places, and things Clustering text Classification, categorization, and tagging Building an example question answering system Untamed text: exploring the next frontier
Book Synopsis Practical Apache Lucene 8 by : Atri Sharma
Download or read book Practical Apache Lucene 8 written by Atri Sharma and published by Apress. This book was released on 2020-11-01 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain a thorough knowledge of Lucene's capabilities and use it to develop your own search applications. This book explores the Java-based, high-performance text search engine library used to build search capabilities in your applications. Starting with the basics of Lucene and searching, you will learn about the types of queries used in it and also take a look at scoring models. Applying this basic knowledge, you will develop a hello world app using basic Lucene queries and explore functions like scoring and document level boosting. Along the way you will also uncover the concepts of partial searching and matching in Lucene and then learn how to integrate geographical information (geospatial data) in Lucene using spatial queries and n-dimensional indexing. This will prepare you to build a location-aware search engine with a representative data set that allows location constraints to be specified during a search. You’ll also develop a text classifier using Lucene and Apache Mahout, a popular machine learning framework. After a detailed review of performance bench-marking and common issues associated with it, you’ll learn some of the best practices of tuning the performance of your application. By the end of the book you’ll be able to build your first Lucene patch, where you will not only write your patch, but also test it and ensure it adheres to community coding standards. What You’ll Learn Master the basics of Apache Lucene Utilize different query types in Apache Lucene Explore scoring and document level boosting Integrate geospatial data into your application Who This Book Is For Developers wanting to learn the finer details of Apache Lucene by developing a series of projects with it.
Download or read book Real-World Hadoop written by Ted Dunning and published by "O'Reilly Media, Inc.". This book was released on 2015-03-24 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you’re a business team leader, CIO, business analyst, or developer interested in how Apache Hadoop and Apache HBase-related technologies can address problems involving large-scale data in cost-effective ways, this book is for you. Using real-world stories and situations, authors Ted Dunning and Ellen Friedman show Hadoop newcomers and seasoned users alike how NoSQL databases and Hadoop can solve a variety of business and research issues. You’ll learn about early decisions and pre-planning that can make the process easier and more productive. If you’re already using these technologies, you’ll discover ways to gain the full range of benefits possible with Hadoop. While you don’t need a deep technical background to get started, this book does provide expert guidance to help managers, architects, and practitioners succeed with their Hadoop projects. Examine a day in the life of big data: India’s ambitious Aadhaar project Review tools in the Hadoop ecosystem such as Apache’s Spark, Storm, and Drill to learn how they can help you Pick up a collection of technical and strategic tips that have helped others succeed with Hadoop Learn from several prototypical Hadoop use cases, based on how organizations have actually applied the technology Explore real-world stories that reveal how MapR customers combine use cases when putting Hadoop and NoSQL to work, including in production
Download or read book NoSQL written by Ganesh Chandra Deka and published by CRC Press. This book was released on 2017-05-19 with total page 471 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the advanced databases for the cloud-based application known as NoSQL. It will explore the recent advancements in NoSQL database technology. Chapters on structured, unstructured and hybrid databases will be included to explore bigdata analytics, bigdata storage and processing. The book is likely to cover a wide range of topics such as cloud computing, social computing, bigdata and advanced databases processing techniques.
Book Synopsis Practical Machine Learning by : Ted Dunning
Download or read book Practical Machine Learning written by Ted Dunning and published by "O'Reilly Media, Inc.". This book was released on 2014 with total page 55 pages. Available in PDF, EPUB and Kindle. Book excerpt: Annotation Building a simple but powerful recommendation system is much easier than you think. Approachable for all levels of expertise, this report explains innovations that make machine learning practical for business production settingsand demonstrates how even a small-scale development team can design an effective large-scale recommendation system. Apache Mahout committers Ted Dunning and Ellen Friedman walk you through a design that relies on careful simplification. Youll learn how to collect the right data, analyze it with an algorithm from the Mahout library, and then easily deploy the recommender using search technology, such as Apache Solr or Elasticsearch. Powerful and effective, this efficient combination does learning offline and delivers rapid response recommendations in real time. Understand the tradeoffs between simple and complex recommendersCollect user data that tracks user actionsrather than their ratingsPredict what a user wants based on behavior by others, using Mahoutfor co-occurrence analysisUse search technology to offer recommendations in real time, complete with item metadataWatch the recommender in action with a music service exampleImprove your recommender with dithering, multimodal recommendation, and other techniques.
Book Synopsis Professional NoSQL by : Shashank Tiwari
Download or read book Professional NoSQL written by Shashank Tiwari and published by John Wiley & Sons. This book was released on 2011-08-31 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: A hands-on guide to leveraging NoSQL databases NoSQL databases are an efficient and powerful tool for storing and manipulating vast quantities of data. Most NoSQL databases scale well as data grows. In addition, they are often malleable and flexible enough to accommodate semi-structured and sparse data sets. This comprehensive hands-on guide presents fundamental concepts and practical solutions for getting you ready to use NoSQL databases. Expert author Shashank Tiwari begins with a helpful introduction on the subject of NoSQL, explains its characteristics and typical uses, and looks at where it fits in the application stack. Unique insights help you choose which NoSQL solutions are best for solving your specific data storage needs. Professional NoSQL: Demystifies the concepts that relate to NoSQL databases, including column-family oriented stores, key/value databases, and document databases. Delves into installing and configuring a number of NoSQL products and the Hadoop family of products. Explains ways of storing, accessing, and querying data in NoSQL databases through examples that use MongoDB, HBase, Cassandra, Redis, CouchDB, Google App Engine Datastore and more. Looks at architecture and internals. Provides guidelines for optimal usage, performance tuning, and scalable configurations. Presents a number of tools and utilities relating to NoSQL, distributed platforms, and scalable processing, including Hive, Pig, RRDtool, Nagios, and more.
Book Synopsis Pro Hadoop Data Analytics by : Kerry Koitzsch
Download or read book Pro Hadoop Data Analytics written by Kerry Koitzsch and published by Apress. This book was released on 2016-12-29 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn advanced analytical techniques and leverage existing tool kits to make your analytic applications more powerful, precise, and efficient. This book provides the right combination of architecture, design, and implementation information to create analytical systems that go beyond the basics of classification, clustering, and recommendation. Pro Hadoop Data Analytics emphasizes best practices to ensure coherent, efficient development. A complete example system will be developed using standard third-party components that consist of the tool kits, libraries, visualization and reporting code, as well as support glue to provide a working and extensible end-to-end system. The book also highlights the importance of end-to-end, flexible, configurable, high-performance data pipeline systems with analytical components as well as appropriate visualization results. You'll discover the importance of mix-and-match or hybrid systems, using different analytical components in one application. This hybrid approach will be prominent in the examples. What You'll Learn Build big data analytic systems with the Hadoop ecosystem Use libraries, tool kits, and algorithms to make development easier and more effective Apply metrics to measure performance and efficiency of components and systems Connect to standard relational databases, noSQL data sources, and more Follow case studies with example components to create your own systems Who This Book Is For Software engineers, architects, and data scientists with an interest in the design and implementation of big data analytical systems using Hadoop, the Hadoop ecosystem, and other associated technologies.
Book Synopsis Ultimate Java for Data Analytics and Machine Learning by : Abhishek Kumar
Download or read book Ultimate Java for Data Analytics and Machine Learning written by Abhishek Kumar and published by Orange Education Pvt Ltd. This book was released on 2024-08-08 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: TAGLINE Empower Your Data Insights with Java's Top Tools and Frameworks. KEY FEATURES ● Explore diverse techniques and algorithms for data analytics using Java. ● Learn through hands-on examples and practical applications in each chapter. ● Master essential tools and frameworks such as JFreeChart for data visualization and Deeplearning4j for deep learning. DESCRIPTION This book is a comprehensive guide to data analysis using Java. It starts with the fundamentals, covering the purpose of data analysis, different data types and structures, and how to pre-process datasets. It then introduces popular Java libraries like WEKA and Rapidminer for efficient data analysis. The middle section of the book dives deeper into statistical techniques like descriptive analysis and random sampling, along with practical skills in working with relational databases (JDBC, SQL, MySQL) and NoSQL databases. It also explores various analysis methods like regression, classification, and clustering, along with applications in business intelligence and time series prediction. The final part of the book gives a brief overview of big data analysis with Java frameworks like MapReduce, and introduces deep learning with the Deeplearning4J library. Whether you're new to data analysis or want to improve your Java skills, this book offers a step-by-step approach with real-world examples to help you master data analysis using Java. WHAT WILL YOU LEARN ● Understand foundational principles and types of data analytics, including descriptive, diagnostic, predictive, and prescriptive analytics. ● Master techniques for preprocessing data, including cleaning and munging, to prepare it for analysis. ● Learn how to create various charts and plots including bar charts, histograms, and scatter plots for effective data visualization. ● Explore Java-based libraries such as WEKA and Deeplearning4j for implementing machine learning algorithms. ● Develop expertise in statistical techniques including hypothesis testing, regression (linear and polynomial), and probability distributions. ● Acquire practical skills in SQL querying and JDBC for relational databases. ● Explore applications in business intelligence and deep learning, including image recognition and natural language processing. WHO IS THIS BOOK FOR? This book is ideal for IT professionals, software developers, and data scientists interested in using Java for data analytics. It is also suitable for students and researchers seeking practical insights into Java-based data analysis. Readers should have a basic understanding of Java programming and fundamental concepts in data analysis. TABLE OF CONTENTS 1. Data Analytics Using Java 2. Datasets 3. Data Visualization 4. Java Machine Learning Libraries 5. Statistical Analysis 6. Relational Databases 7. Regression Analysis 8. Classification Analysis 9. Sentiment Analysis 10. Cluster Analysis 11. Working with NoSQL Databases 12. Recommender Systems 13. Applications of Data Analysis 14. Big Data Analysis with Java 15. Deep Learning with Java Index
Book Synopsis Mastering Hadoop by : Sandeep Karanth
Download or read book Mastering Hadoop written by Sandeep Karanth and published by Packt Publishing Ltd. This book was released on 2014-12-29 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: Do you want to broaden your Hadoop skill set and take your knowledge to the next level? Do you wish to enhance your knowledge of Hadoop to solve challenging data processing problems? Are your Hadoop jobs, Pig scripts, or Hive queries not working as fast as you intend? Are you looking to understand the benefits of upgrading Hadoop? If the answer is yes to any of these, this book is for you. It assumes novice-level familiarity with Hadoop.
Book Synopsis Big Data Analytics by : Arun K. Somani
Download or read book Big Data Analytics written by Arun K. Somani and published by CRC Press. This book was released on 2017-10-30 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proposed book will discuss various aspects of big data Analytics. It will deliberate upon the tools, technology, applications, use cases and research directions in the field. Chapters would be contributed by researchers, scientist and practitioners from various reputed universities and organizations for the benefit of readers.
Book Synopsis Kubeflow for Machine Learning by : Trevor Grant
Download or read book Kubeflow for Machine Learning written by Trevor Grant and published by "O'Reilly Media, Inc.". This book was released on 2020-10-13 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you're training a machine learning model but aren't sure how to put it into production, this book will get you there. Kubeflow provides a collection of cloud native tools for different stages of a model's lifecycle, from data exploration, feature preparation, and model training to model serving. This guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data engineers how to make models scalable and reliable. Using examples throughout the book, authors Holden Karau, Trevor Grant, Ilan Filonenko, Richard Liu, and Boris Lublinsky explain how to use Kubeflow to train and serve your machine learning models on top of Kubernetes in the cloud or in a development environment on-premises. Understand Kubeflow's design, core components, and the problems it solves Understand the differences between Kubeflow on different cluster types Train models using Kubeflow with popular tools including Scikit-learn, TensorFlow, and Apache Spark Keep your model up to date with Kubeflow Pipelines Understand how to capture model training metadata Explore how to extend Kubeflow with additional open source tools Use hyperparameter tuning for training Learn how to serve your model in production
