There was a discussion on Hadoop Comic in Apache Hadoop Mailing list. I found that Maneesh Varshney created a wonderful comic strip to describe the entire Hadoop Distributed File System (HDFS). It is quite useful to understand the HDFS in easy way.
I am sharing the comic at my Slideshare account.
Kudos to Maneesh Varshney for the wonderful and creative work.
Here it is .
Apache Mahout is an Open Source scalable Machine Learning library in Java. It is designed to handle large data set. More than a dozen of Machine Learning and Data Mining algorithms are available in Mahout. All those algorithms are implemented on top of Apache Hadoop. The framework is distributed under a commercially friendly Apache License. It helps researchers and corporate to build scalable and practical products based on Machine Learning and Data Mining Principles. A wide range of big companies as well as startups are using Apache Mahout in their products.
The Apache Mahout project is focused three interesting Machine Learning problems 1) recommendation systems 2) clustering and 3) classification. The project address real world practical problems. The tool makes life of Machine Learning Developers much enjoyable. The book "Mahout in Action" by Sean Owen,Robin Anil, Ted Dunning and Ellen Friedman introduces the wonder world of creating scalable and real world machine learning projects with Apache Mahout. It is written in a lucid language so that a beginner in Machine Learning can understand the concepts and kick start working with classification, clustering or recommendation projects. Even though the detailed algorithmic back ground of underlying algorithms in Mahout is not described the logic (common sense) behind the system is explained very well with help of code examples and practical projects. I am giving chapter wise overview of the book "Mahout in Action" below. A sample chapter is availeble for download at http://www.manning.com/free/green_owen.html
Chapter 1 of the book get you introduced to Mahout. Through this chapter you get to know the history of Mahout project, algorithms, it's capabilities and configurations.
Chapter 2 of the book introduces recommendation systems to the reader. The chapter teaches how to build a basic re commender systems with Apache Mahout. The examples given for narrating the technique is very clear and understandable to all.
Chapter 3 of the book discuss about data representation for building a recommender engine. The discussions in this chapter extends up to some naive data structure in Mahout. There is some discussion on using MySQL for storing data for building recommender engines.
Chapter 4 of the book gives more insight in to building scalable recommender systems. It introduces user based recommendation engines as well as item based recommendation engines. The examples are very clear and it helps practitioners to build better prototypes much faster. The chapter is written in such a lucid way that any body can understand the common sense behind the recommender engines.
The fifth chapter of the book deals with producing a full fledged recommender system with Apache Mahout. The discussion and examples in this chapter extends up to deploying a web based recommeder engine. Once u covered up this chapter it can be ensured that you can build a good production quality recommender engine for your client.
Chapter 6 of the book discussed how to build a scalable and distributed recommendation system with Mahout and Hadoop frame work. The chapter gives illustrative example for the task with Wikipedia data set. The author spent some pages for explaining Map Reduce concept in a much lucid way. There is a discussion on running the recommender in a cloud platform too. This chapter is definitely a helping point for professionals to kick start their recommender projects with less pain.
Starting from chapter 7 to 12 the book discusses about Clustering techniques using Apache Mahout. Chapter seven gives a brief introduction to clustering with practical examples. The chapter contains discussions on different clustering algorithms available in Mahout.
Chapter eight of the book deals with preparing and representing data for clustering task. Tips and tricks for converting raw data to vectors for clustering is discussed in a very lucid manner in this chapter.
The 9th chapter of the book discusses details on clustering algorithms in Mahout. The major algorithms covered in this chapter are K-Means clustering, Centroid generation using Canopy clustering, Fuzzy K-Means clustering, Dirichlet clustering,Topic modeling using LDA as a variant of clustering. There is a small cases study on clustering news items using Apache Mahout. One of my project student has undertaken such a project for his MSc in CS .
The 10th chapter is focused on evaluation of clustering system. The chapter discusses about clustering output inspection, quality evaluation of clustering and improving the quality of clusters.
The 11th chapter deals with producing a scalable clustering system with Mahout. It gives good insight in to the art of content clustering with two case studies. The 12th chapter discusses some use cased of clustering with code examples including twitter user clustering, playing with last.fm data and clustering.
Beginning from chapter 13 to end of chapter 16 the book discusses about the technique of classification. Chapter 13 of the book gives introduction to classification. It explains classification step by step with examples.The illustrations given in the chapter makes the content more enjoyable and understanding for the reader. Chapter 14 deals with training a classifier system. It explains the task of training with a publically available data-set called 20 newsgroups data set. There is a discussion on selecting algorithm for the classification task too. When ever I came to know about Mahout I used the classification techniques and algorithms. Chapter 16 has a wonderful discussion on deployment of classification system. The section gives practical insight on pros and cons of developing and deploying scalable classification system that can be bench marked with existing best performing systems.
The 17th Chapter needs special mention. The chapter is a case study named "Case study: Shop It To Me". The discussions in this chapter shows real power of Apache Mahout with the help of a practical project.
There are two appendix provided to the book. Appendix A deals with some JVM tuning tips and tricks for Deploying Hadoop/Mahout based projects. It is even useful for core Java programmers too. The Appendix B gives insight on "Mahout Math" and some deep math related stuff in Mahout.
The book is available from Manning MEAP site. Three excerpts are available in the web site along with sample code. This is a must-read for all Machine Learning and NLP Developers and Researchers. This is an excellent book and I am very much happy to read practice and understand the Apache Mahout in such detail. Kudos to Sean Owen,Robin Anil, Ted Dunning and Ellen Friedman.
For code samples and sample chapters visit http://www.manning.com/free/green_owen.html