Machine Learning with Mahout Certification Training

Learn to devise scalable Machine Learning applications

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Key Highlights

  • Self-paced learning
  • Case Studies on Real-life Scenarios
  • Lifetime Access to Learning Management System
  • Practice Assignments
  • 24X7 Expert Support
  • Online Forum for Discussions

Course Price

$199.00 $399.00

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Available Courses Delivery

This course is available in the following formats:

Self Paced (On-Demand)

24x7 access to instructor-led videos and practical activities
Convenient training that syncs with your schedule
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$399  $199

Course Overview

This course is devised to educate learners about the development of scalable Machine Learning algorithms using Apache Mahout. It is designed to provide an integrated package of Machine Learning and Big Data using Apache Mahout. It aims to train learners in instantly executing their own algorithms.

Course Objectives

  • Teach various Machine Learning techniques using Mahout
  • Skill learners at implementing Recommender using MapReduce
  • Educate about the execution of Collaborative Filtering
  • Equip learners with the understanding of algorithms of SVM, Random Forests, Naive Bayes, etc.
  • Acquaint learners about Clustering and Categorization
  • Impart practical knowledge for analyzing Big Data using Hadoop and Mahout
  • Educate learners about various tools, such as Matlab, SAS, Weka, Octave, etc

Career Benefits

  • Opportunity to work as a Mahout Expert
  • Become an expert in identifying solutions of all data-related issues
  • Multiple job opportunities
  • Higher paycheck


  • Basic knowledge of Java will be an added advantage
  • Understanding of Hadoop is required

Who should take up?

  • Data Scientists
  • Software Developers
  • Software Architects
  • Business Analysts
  • Statisticians
  • Graduates who want to enter analytics field
  • Professionals using Python, R, Matlab

Course Content

  • Machine Learning Fundamentals
  • Apache Mahout Basics
  • History of Mahout
  • Supervised and Unsupervised Learning techniques
  • Mahout and Hadoop
  • Introduction to Clustering
  • Classification
  • Mahout on Apache Hadoop setup
  • Mahout and Myrrix
  • Recommendations using Mahout
  • Introduction to Recommendation systems
  • Content-Based (Collaborative filtering, Nearest N Users, Threshold, User-based Item-based)
  • Mahout Optimizations
  • 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)
  • Recommendors in Production
  • 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
  • Examples
  • Basics
  • Predictor variables and Target variables
  • Common Algorithms
  • SGD
  • SVM
  • Navie Bayes
  • Random Forests
  • Training and evaluating a Classifier
  • Developing a Classifier
  • Mahout on Amazon EMR
  • Mahout Vs R
  • Introduction to tools like Weka, Octave, Matlab, SAS
  • A complete recommendation engine construct on application logs and transactions


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