MAHOUT

→ 1. Introduction to Mahout

  • Mahout overview
  • Mahout's machine learning themes
  • Large scale handling with Mahout and Hadoop
  • Setting up Mahout

→ 2. Introduction to Recommendations

  • Recommendation Overview
  • Building a recommender engine
  • Evaluating a recommender

→ 3. Representing recommender data

→ 4. Making recommendations

  • Exploring user-based recommendation
  • Exploring similarity metrics
  • Item-based recommendations
  • Slope-one recommendation
  • Comparison of recommenders

→ 5. Taking recommenders to production

  • Finding an effective recommender
  • Recommending to anonymous users

→ 6. Recommendation computations using Hadoop

→ 7. Clustering

  • Clustering basics
  • Measuring similarity of items
  • Running Clustering example
  • Exploring distance measures
  • Representing data

→ 8. Clustering algorithms in Mahout

→ 9. Running Clustering on Hadoop

→ 10. Real world applications of Hadoop

→ 11. Introduction to Classification

  • Mahout for classification
  • Classification system overview
  • Classification process
  • Example of Classification

→ 12. Training a classifier

→ 13. Evaluating and tuning a classifier

→ 14. Deploying a classifier

→ 15. Real world examples

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