Machine Learning with Mahout

Mahout & Machine Learning

Mahout Machine Learning: “Mahout Machine Learning is Programming Computers to optimize a Performance Criterion using Example Data or Past Experience”

mahout machine learning training

COURSE OUTLINE:

This Mahout Machine Learning Training will introduce you to the fundamentals of machine learning, and where Mahout fits in the Hadoop ecosystem. The course will provide a blend of Machine Learning Techniques, recommendation system, and Mahout on Hadoop and Amazon EMR.

1. Gain an insight into the Machine Learning techniques.

2. Understand various Machine Learning Techniques and how to implement these using ‘Apache Mahout’.

3. Understand the recommendation system.

4. Learn Collaborative filtering, Clustering and Categorization.

5. Overview of recommendation platform.

6. Analyse Big Data using Hadoop and Mahout.

7. Implementing a recommender using MapReduce.

 

PREREQUISITES:

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
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