Machine Learning with Mahout Certification Training

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Machine Learning with Mahout Certification Training


 (4.9) | 450 Ratings


Introduction


Machine Learning with Mahout Certification 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 Apache Mahout

Learning Objectives - This module will give you an insight about what 'Machine Learning' is and How Apache Mahout algorithms are used in building intelligent applications.

Topics - Machine Learning Fundamentals, Apache Mahout Basics, History of Mahout, Supervised and Unsupervised Learning techniques, Mahout and Hadoop, Introduction to Clustering, Classification.

Mahout and Hadoop

Learning Objectives - In this module you will learn how to set up Mahout on Apache Hadoop. You will also get an understanding of Myrrix Machine Learning Platform.

Topics - Mahout on Apache Hadoop setup, Mahout and Myrrix.

Recommendation Engine

Learning Objectives - In this module you will get an understanding of the recommendation system in Mahout and different filtering methods.

Topics - 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

Learning Objectives - In this module you will learn about the Recommendation platforms and implement a Recommender using MapReduce.

Topics - 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

Learning Objectives - This module will help you in understanding 'Clustering' in Mahout and also give an overview of common Clustering Algorithms.

Topics - 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

Learning Objectives - In this module you will get a clear understanding of Classifier and the common Classifier Algorithms.

Topics - 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

Learning Objectives - At the end of this module, you will get an understanding of how Mahout can be used on Amazon EMR Hadoop distribution.

Topics - Mahout on Amazon EMR, Mahout Vs R, Introduction to tools like Weka, Octave, Matlab, SAS.

Project

Learning Objectives - In this module you will develop an intelligent application using Mahout on Hadoop.

Topics - A complete recommendation engine built on application logs and transactions.

Exam & Certification

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Course Curriculum

Introduction to Machine Learning and Apache Mahout

Learning Objectives - This module will give you an insight about what 'Machine Learning' is and How Apache Mahout algorithms are used in building intelligent applications.

Topics - Machine Learning Fundamentals, Apache Mahout Basics, History of Mahout, Supervised and Unsupervised Learning techniques, Mahout and Hadoop, Introduction to Clustering, Classification.

Mahout and Hadoop

Learning Objectives - In this module you will learn how to set up Mahout on Apache Hadoop. You will also get an understanding of Myrrix Machine Learning Platform.

Topics - Mahout on Apache Hadoop setup, Mahout and Myrrix.

Recommendation Engine

Learning Objectives - In this module you will get an understanding of the recommendation system in Mahout and different filtering methods.

Topics - 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

Learning Objectives - In this module you will learn about the Recommendation platforms and implement a Recommender using MapReduce.

Topics - 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

Learning Objectives - This module will help you in understanding 'Clustering' in Mahout and also give an overview of common Clustering Algorithms.

Topics - 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

Learning Objectives - In this module you will get a clear understanding of Classifier and the common Classifier Algorithms.

Topics - 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

Learning Objectives - At the end of this module, you will get an understanding of how Mahout can be used on Amazon EMR Hadoop distribution.

Topics - Mahout on Amazon EMR, Mahout Vs R, Introduction to tools like Weka, Octave, Matlab, SAS.

Project

Learning Objectives - In this module you will develop an intelligent application using Mahout on Hadoop.

Topics - A complete recommendation engine built on application logs and transactions.

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