Mahout - An algorithm library for scalable machine learning on Hadoop

APACHE MAHOUT
An algorithm library for scalable machine learning on Hadoop

Apache™ Mahout is a library of scalable machine-learning algorithms, implemented on top of Apache Hadoop®  and using the MapReduce paradigm.


Mahout provides the data science tools to automatically find meaningful patterns in big data sets stored on the Hadoop Distributed File System (HDFS)


WHAT MAHOUT DOES
Mahout supports four main data mining use cases:

Collaborative filtering – Based on user behavior, makes product recommendations (e.g. YouTube recommended movies)
Clustering – takes items in a particular class (such as web pages or newspaper articles) and organizes them into groups or clusters, such that items belonging to the same group are similar to each other
Classification – learns from existing categorizations and then assigns unclassified items to the best category
Frequent itemset mining – analyzes items in a group (e.g. items in a shopping cart) and then identifies which items typically appear together



http://mahout.apache.org/users/basics/algorithms.html


Machine Learning with Mahout -Tutorial
KnowBigData
___________________

___________________

Apache Mahout Tutorial-1 | Apache Mahout Tutorial for Beginners-1
edureka!
___________________

___________________

Stock Price Movement Prediction Using Mahout and Pydoop’s Website for Big Data Analytics course
http://stock-price-movement-prediction.readthedocs.io/en/latest/