This course will prepare you to:

• Explain the architecture of the APACHE Mahout component.
• Configure and use new functionalities in APACHE Mahout.
• Use the standard APACHE Mahout Sub Modules.
• Explain the APACHE Mahout Controlling Configuration and Customization option.

• 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.

• 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

• 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

Practice Test and Interview Questions