Machine Learning with Mahout - Self-Pacededureka
$ 179 - (Rs 11,704)
1. 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.2. 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.3. 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.4. 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.5. 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.6. 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.7. 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.8. 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.