EncartaLabs

Apache Mahout - Advanced

( Duration: 2 Days )

As 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.

COURSE AGENDA

1

Recommendation Engine

2

Intro to recommendation systems

3

Content Based

4

Collaborative filtering

5

User based

6

Threshold

7

Item based

8

Mahout Optimizations

9

An overview of a recommendation platform

  • Similarity measures
  • Manhattan distance
  • Euclidean distance
  • Cosine Similarity
  • Pearson’s Correlation Similarity
  • Loglikihood Similarity
10

Tanimoto

11

Evaluating Recommendation engines

  • Online
  • Offline
12

Intro to Clustering

  • Common Clustering Algorithms
  • K-means
  • Fuzzy K-means, Mean Shift etc
  • Representing data
  • Feature Selection
  • Vectorization
  • Representing Vectors
13

Intro to Classification

14

Common Algorithms

  • Mahout on Hadoop
  • Apache Mahout & Myrrix
15

Mahout on Amazon EMR

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