Learning Apache Mahout

In the past few years the generation of data and our capability to store and process it has grown exponentially. There is a need for scalable analytics frameworks and people with the right skills to get the information needed from this Big Data. Apache Mahout is one of the first and most prominent Big Data machine learning platforms. It implements machine learning algorithms on top of distributed processing platforms such as Hadoop and Spark.

Starting with the basics of Mahout and machine learning, you will explore prominent algorithms and their implementation in Mahout development. You will learn about Mahout building blocks, addressing feature extraction, reduction and the curse of dimensionality, delving into classification use cases with the random forest and Naive Bayes classifier and item and user-based recommendation. You will then work with clustering Mahout using the K-means algorithm and implement Mahout without MapReduce. Finish with a flourish by exploring end-to-end use cases on customer analytics and test analytics to get a real-life practical know-how of analytics projects.


1121660612
Learning Apache Mahout

In the past few years the generation of data and our capability to store and process it has grown exponentially. There is a need for scalable analytics frameworks and people with the right skills to get the information needed from this Big Data. Apache Mahout is one of the first and most prominent Big Data machine learning platforms. It implements machine learning algorithms on top of distributed processing platforms such as Hadoop and Spark.

Starting with the basics of Mahout and machine learning, you will explore prominent algorithms and their implementation in Mahout development. You will learn about Mahout building blocks, addressing feature extraction, reduction and the curse of dimensionality, delving into classification use cases with the random forest and Naive Bayes classifier and item and user-based recommendation. You will then work with clustering Mahout using the K-means algorithm and implement Mahout without MapReduce. Finish with a flourish by exploring end-to-end use cases on customer analytics and test analytics to get a real-life practical know-how of analytics projects.


39.99 In Stock
Learning Apache Mahout

Learning Apache Mahout

by Chandramani Tiwary
Learning Apache Mahout

Learning Apache Mahout

by Chandramani Tiwary

eBook

$39.99 

Available on Compatible NOOK devices, the free NOOK App and in My Digital Library.
WANT A NOOK?  Explore Now

Related collections and offers


Overview

In the past few years the generation of data and our capability to store and process it has grown exponentially. There is a need for scalable analytics frameworks and people with the right skills to get the information needed from this Big Data. Apache Mahout is one of the first and most prominent Big Data machine learning platforms. It implements machine learning algorithms on top of distributed processing platforms such as Hadoop and Spark.

Starting with the basics of Mahout and machine learning, you will explore prominent algorithms and their implementation in Mahout development. You will learn about Mahout building blocks, addressing feature extraction, reduction and the curse of dimensionality, delving into classification use cases with the random forest and Naive Bayes classifier and item and user-based recommendation. You will then work with clustering Mahout using the K-means algorithm and implement Mahout without MapReduce. Finish with a flourish by exploring end-to-end use cases on customer analytics and test analytics to get a real-life practical know-how of analytics projects.



Product Details

ISBN-13: 9781783555222
Publisher: Packt Publishing
Publication date: 03/30/2015
Sold by: Barnes & Noble
Format: eBook
Pages: 250
File size: 5 MB

About the Author

Chandramani Tiwary is a Data Scientist with special interest towards building data driven products. He is a founding member of the Data Science team at Gainsight and was instrumental in framing Data Science use cases for customer success management in the SaaS industry. He is also an advisor to multiple organizations. He can be followed on Twitter at https://twitter.com/Mani_on_Twitt and on LinkedIn at https://www.linkedin.com/today/author/60666042.
From the B&N Reads Blog

Customer Reviews