Machine Learn ONLINE TRAINING

Machine Learn COURSE CONTENT

    Introduction to Machine Learning and Apache Mahout

  • Machine Learning Fundamentals
  • Apache Mahout Basics
  • History of Mahout
  • Supervised and Unsupervised Learning techniques
  • Mahout and Hadoop
  • Introduction to Clustering
  • Classification
  • Mahout and Hadoop

  • Mahout on Apache Hadoop setup
  • Mahout and Myrrix
  • Recommendation Engine

  • Recommendations using Mahout
  • Introduction to Recommendation systems
  • Content Based (Collaborative filtering, User based, Nearest N Users, Threshold, Item based)
  • Mahout Optimizations
  • Implementing a recommender and recommendation platform

  • User based recommendation
  • User Neighbourhood
  • Item based Recommendation
  • Implementing a Recommender using MapReduce
  • Platforms: Similarity Measures, Manhattan Distance, Euclidean Distance, Cosine Similarity, Pearson's Correlation Similarity, Loglikihood Similarity, Tanimoto
  • Evaluating Recommendation Engines (Online and Offline)
  • Recommenders in Production
  • Clustering

  • Clustering
  • Common Clustering Algorithms
  • K-means
  • Canopy Clustering
  • Fuzzy K-means and Mean Shift etc.
  • Representing Data
  • Feature Selection
  • Vectorization
  • Representing Vectors
  • Clustering documents through example
  • TF-IDF
  • Implementing clustering in Hadoop
  • Classification.
  • Classification

  • Examples
  • Basics
  • Predictor variables and Target variables
  • Common Algorithms
  • SGD
  • SVM
  • Navie Bayes
  • Random Forests
  • Training and evaluating a Classifier
  • Developing a Classifier
  • Mahout and Amazon EMR

  • Mahout on Amazon EMR
  • Mahout Vs R
  • Introduction to tools like Weka
  • Octave, Matlab, SAS

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