In Mahout Training, you will know what is machine learning, what is Apache mahout 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.

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.

This module will focus on Recommendation algorithm and Mahout optimizations.

Recommendations using Apache Mahout
Introduction to Recommendation systems
Content Based Mahout Optimizations.

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.

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.

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

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

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.