Advanced Apache Mahout Training

Once the exclusive domain of academics and corporations with large research budgets, intelligent applications that learn from data and user input are becoming more common. The need for machine-learning techniques like clustering, Mahout On Amazon EMR, Mahout with Apache Hadoop, 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. The Apache Mahout project aims to make building intelligent applications easier and faster.

Day 1

  • Recommendation Engine
  • Intro to recommendation systems
  • Content Based
  • Collaborative filtering
  • User based
  • Threshold
  • Item based
  • Mahout Optimizations
  • 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

Day 2

  • Intro to Clustering
    • Common Clustering Algorithms
    • K-means
    • Fuzzy K-means, Mean Shift etc
    • Representing data
    • Feature Selection
    • Vectorization
    • Representing Vectors
  • Intro to Classification
  • Basics
  • Common Algorithms
  • Setting up ActiveMQ
    • Mahout on Hadoop
    • Apache Mahout & Myrrix
  • Mahout on Amazon EMR

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