MAHOUT MACHINE LEARNING TRAINING

The Apache Mahout machine learning library’s goal is to build scalable machine learning libraries.

Mahout Machine Learning Training

Mahout currently has

  • Collaborative Filtering
  • User and Item based recommenders
  • K-Means, Fuzzy K-Means clustering
  • Mean Shift clustering
  • Dirichlet process clustering
  • Latent Dirichlet Allocation
  • Singular value decomposition
  • Parallel Frequent Pattern mining
  • Complementary Naive Bayes classifier
  • Random forest decision tree based classifier
  • High performance java collections (previously colt collections)
  • A vibrant community

Scalable ?

Scalable to reasonably large data sets. Our core algorithms for clustering, classfication and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for non-distributed algorithms

Scalable to support your business case. Mahout is distributed under a commercially friendly Apache Software license.

Scalable community. The goal of Mahout is to build a vibrant, responsive, diverse community to facilitate discussions not only on the project itself but also on potential use cases. Come to the mailing lists to find out more.

Currently Mahout supports mainly four use cases: Recommendation mining takes users’ behavior and from that tries to find items users might like. Clustering takes e.g. text documents and groups them into groups of topically related documents. Classification learns from existing categorized documents what documents of a specific category look like and is able to assign unlabelled documents to the correct category. Frequent itemset mining takes a set of item groups (terms in a query session, shopping cart content) and identifies, which individual items usually appear together.

Mahout has come a long way in a short amount of time. Although the project’s focus is still on what I like to call the “three Cs” — collaborative filtering (recommenders), clustering, and classification — the project has also added other capabilities. I’ll highlight a few key expansions and improvements in two areas: core algorithms (implementations) for machine learning, and supporting infrastructure including input/output tools, integration points with other libraries, and more examples for reference. Do note, however, that this status is not complete. Furthermore, the limited space of this article means I can only offer a few sentences on each of the improvements. I encourage readers to find more information by reading the News section of the Mahout website and the release notes for each of Mahout’s releases.

Scalable, commercial-friendly machine learning for building intelligent applications

This course will introduce you to the basic blocks of machine learning, and where Mahout fits in. We will majorly be looking at recommendation systems, what are their types, how to choose a similarity algorithm, and a typical design of a recommendation system. We will be exploring many examples of recommendations in the real world. We will also be running recommendations for a pretty large dataset on Hadoop over Amazon Elastic MapReduce.
We will also be looking at the basics of Clustering, a typical Clustering algorithm, with an example.
Lastly, we will look into the basics of Classification, it’s types and some examples.

The need for machine-learning techniques like clustering, collaborative filtering, and categorization has never been greater, be it for finding commonalities among large groups of people or automatically tagging large volumes of Web content.

MAHOUT MACHINE LEARNING TRAINING

About

The Apache Mahout project aims to make building intelligent applications easier and faster. Mahout co-founder Grant Ingersoll introduces the basic concepts of machine learning and then demonstrates how to use Mahout to cluster documents, make recommendations, and organize content.

Apache Mahout is an Apache project to produce free implementations of distributed or otherwise scalable machine learning algorithms focused primarily in the areas of collaborative filtering, clustering and classification, often leveraging, but not limited to, the Hadoop platform.

The Apache Mahout project aims to make building intelligent applications easier and faster. Mahout co-founder Grant Ingersoll introduces the basic concepts of machine learning and then demonstrates how to use Mahout to cluster documents, make recommendations, and organize content.

Three specific machine-learning tasks that Mahout currently implements.

  1. Collaborative filtering
  2. Clustering
  3. Categorization

The need for machine-learning techniques like clustering, collaborative filtering, and categorization has never been greater, be it for finding commonalities among large groups of people or automatically tagging large volumes of Web content.

Agenda
  • Intro to Machine Learning
  • Intro to Apache Mahout
  • Recommendation Engine
  • Clustering
  • Classification
  • Intro to recommendation systems
  • Content BasedMahout Optimizations
    • Collaborative filtering
    • User based
    • Nearest N Users
    • Threshold
    • Item based
  • An overview of a recommendation platform
    • Similarity measures
    • Manhattan distance
    • Euclidean distance
    • Cosine Similarity
    • Pearson’s Correlation Similarity
    • Loglikihood Similarity
    • Tanimoto
  • Evaluating Recommendation engines
    • Online
    • Offline
  • Intro to Clustering
    • Common Clustering Algorithms
    • K-means
    • Fuzzy K-means, Mean Shift etc
    • Representing data
    • Feature Selection
    • Vectorization
    • Representing Vectors
  • Intro to ClassificationMahout on Hadoop
    • Examples
    • Basics
    • Common Algorithms
  • Apache Mahout & Myrrix

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