Apache Mahout Online Training

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Apache Mahout Online Training


 (4.9) | 350 Ratings


Introduction


Apache Mahout Online Training Details
Track Regular Track Weekend Track Fast Track
Course Duration 35 Hrs 8 Weekends 5 Days
Hours 1hr/day 2 Hours a day 6 Hours a day
Training Mode Online Classroom Online Classroom Online Classroom
Delivery Instructor Led-Live Instructor Led-Live Instructor Led-Live


Course Curriculum

Introduction to Machine Learning and Mahout

  • In Mahout Training, you will know what machine learning is, what Apache mahout is and what is clustering.
  • Machine Learning Fundamentals
  • Apache Mahout Basics
  • History of Mahout
  • Supervised and Unsupervised Learning techniques
  • Mahout and Hadoop
  • Introduction to Clustering and Classification.

Apache Mahout And Hadoop

  • Myrrix is a recommendation engine based on mahout, therefore this module is designed for mahout training and myrrix.
  • Mahout on Apache Hadoop
  • Setup Mahout and Myrrix.

Recommendation Engine In Mahout Training

  • This module will focus on Recommendation algorithm and Mahout optimizations.
  • Recommendations using Apache Mahout
  • Introduction to Recommendation systems
  • Content Based Mahout Optimizations.

Implementing A Recommender And Recommendation Platform

  • Understanding the various recommendations, implementing Recommendors, different types of similarities in Apache mahout.
  • 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
  • Log likelihood Similarity
  • Tanimoto Evaluating
  • Recommendation Engines (Online and Offline)
  • Recommendors in Production.

Clustering

  • This module is designed to give you thoroughly over the clustering concepts.
  • Clustering
  • Common Clustering Algorithms in Apache mahout training
  • K-means Canopy Clustering
  • Fuzzy K-means and Mean Shift etc.
  • Representing Data Feature Selection
  • Vectorization in Apache Mahout training
  • Representing Vectors
  • Clustering documents through example TF-IDF and Implementing clustering in Hadoop Classification.

Classification

  • By the end of this training module, you will be able to develop a classifier on your own.
  • Examples
  • Basic Predictor variables and Target variables
  • Common Algorithms
  • SGD
  • SVM
  • Navie Bayes
  • Random Forests
  • Training and evaluating a Classifier
  • Developing a Classifier

Apache Mahout and Amazon EMR

  • We’ll focus on Apache Mahout and Amazon EMR, have an overview on Weka, Octave Matlab and SAS.
  • Mahout on Amazon
  • EMR Mahout Vs R
  • Introduction to tools like Weka, Octave, Matlab and SAS

Project Included In Mahout Training

  • This is the implementation module, of what we have learnt so far in Apache Mahout training.
  • A complete recommendation engine is built on application logs and transactions.

 

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Course Curriculum

Introduction to Machine Learning and Mahout

  • In Mahout Training, you will know what machine learning is, what Apache mahout is and what is clustering.
  • Machine Learning Fundamentals
  • Apache Mahout Basics
  • History of Mahout
  • Supervised and Unsupervised Learning techniques
  • Mahout and Hadoop
  • Introduction to Clustering and Classification.

Apache Mahout And Hadoop

  • Myrrix is a recommendation engine based on mahout, therefore this module is designed for mahout training and myrrix.
  • Mahout on Apache Hadoop
  • Setup Mahout and Myrrix.

Recommendation Engine In Mahout Training

  • This module will focus on Recommendation algorithm and Mahout optimizations.
  • Recommendations using Apache Mahout
  • Introduction to Recommendation systems
  • Content Based Mahout Optimizations.

Implementing A Recommender And Recommendation Platform

  • Understanding the various recommendations, implementing Recommendors, different types of similarities in Apache mahout.
  • 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
  • Log likelihood Similarity
  • Tanimoto Evaluating
  • Recommendation Engines (Online and Offline)
  • Recommendors in Production.

Clustering

  • This module is designed to give you thoroughly over the clustering concepts.
  • Clustering
  • Common Clustering Algorithms in Apache mahout training
  • K-means Canopy Clustering
  • Fuzzy K-means and Mean Shift etc.
  • Representing Data Feature Selection
  • Vectorization in Apache Mahout training
  • Representing Vectors
  • Clustering documents through example TF-IDF and Implementing clustering in Hadoop Classification.

Classification

  • By the end of this training module, you will be able to develop a classifier on your own.
  • Examples
  • Basic Predictor variables and Target variables
  • Common Algorithms
  • SGD
  • SVM
  • Navie Bayes
  • Random Forests
  • Training and evaluating a Classifier
  • Developing a Classifier

Apache Mahout and Amazon EMR

  • We’ll focus on Apache Mahout and Amazon EMR, have an overview on Weka, Octave Matlab and SAS.
  • Mahout on Amazon
  • EMR Mahout Vs R
  • Introduction to tools like Weka, Octave, Matlab and SAS

Project Included In Mahout Training

  • This is the implementation module, of what we have learnt so far in Apache Mahout training.
  • A complete recommendation engine is built on application logs and transactions.

 

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