Learning Objectives- This lesson will teach you about Mahout's recommendation system and several filtering approaches.

Topics- Recommendations for using Mahout, An Overview of Recommendation Systems, Mahout Optimizations, Content Based (Collaborative Filtering, User Based, Nearest N Users, Threshold, Item Based).

Learning Objectives- This session will teach you about recommendation platforms and how to build a recommender using MapReduce.

Topics - User Recommendation, User Neighborhood, Item Recommendation Using MapReduce to implement a Recommender, Platforms: Manhattan Distance, Euclidean Distance, Cosine Similarity, Pearson's Correlation Similarity, Likelihood Similarity, Tanimoto, Evaluating Recommendation Engines (Online and Offline), Production Recommendors.

Learning Objectives -  This module will assist you in comprehending 'Clustering' in Mahout as well as providing an overview of typical Clustering Algorithms.

Topics - Clustering, Common Clustering Algorithms, K-means, Canopy Clustering, Fuzzy K-means, and Mean Shift, among other things, Data Representation, Feature Selection, Vectorization, Vector Representation Document clustering using TF-IDF, for example Clustering in Hadoop, Classification.

Learning Objectives - This lesson will provide you with a thorough grasp of Classifiers and popular Classifier Algorithms.

Topics- Examples, Basics, Predictor variables and Target variables, Common Algorithms, SGD, SVM, Naive Bayes, Random Forests, Training and evaluating a Classifier, Developing a Classifier.

Learning Objectives  You will grasp how to utilize Mahout on Amazon EMR Hadoop distribution at the conclusion of this module.

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

Learning Objectives  You will grasp how to utilize Mahout on Amazon EMR Hadoop distribution at the conclusion of this module.

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