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Mahout Machine Learning Training

This Mahout Machine Learning Training 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.

Mahout Machine Learning

REREQUISITES:

Mandatory:

  •    Programming skills in Java (or similar modern programming language)

  •     Basic understanding of Hadoop architecture

  •     Basic understanding of Hadoop MapReduce for data processing at scale

Useful, but not required:

  •     Apache Pig programming

  •     Prior experience with Apache Solr search engine

  •     Matrix algebra

 

Course Outline:

   Mahout Overview

   Mahout Installation

   Introduction to the Math Library

   Vector implementation and Operations (Hands-on exercise)

   Matrix Implementation and Operations (Hands-on exercise)

   Anatomy of a Machine Learning Application

   Classification

  •        Introduction to Classification
  •        Classification Workflow
  •        Feature Extraction
  •        Classification Techniques (Hands-on exercise)
  •        Evaluation (Hands-on exercise)

   Clustering

  •        Use Cases
  •        Clustering algorithms in Mahout
  •        K-means clustering (Hands-on exercise)
  •        Canopy clustering (Hands-on exercise)
  •        Mixture Models
  •        Probabilistic Clustering – Dirichlet (Hands-on exercise)
  •        Latent Dirichlet Model (Hands-on exercise)
  •        Evaluating and Improving Clustering quality (Hands-on exercise)
  •        Distance Measures (Hands-on exercise)

   Recommendation Systems

  •        Overview of Recommendation Systems
  •        Use cases
  •        Types of Recommendation Systems
  •        Collaborative Filtering (Hands-on exercise)
  •        Recommendation System Evaluation (Hands-on exercise)
  •        Similarity Measures
  •        Architecture of Recommendation Systems

Big Data Training Bangalore Hadoop Training in Bangalore, 2013