Apache Mahout Online Training
Introduction to Apache Mahout Apache Mahout is an Apache TLP project to build powerful scalable machine learning tools for use on analyzing big-data on distributed manner. Machine learning is the discipline of artificial intelligence that enables to learn on data, spam filtering and natural language processing. Apache mahout enable clustering, dimensionality reduction and miscellaneous. It is used by Facebook, Linkedin and twitter.
Course Curriculum Unit 1: Introduction to Machine Learning and Apache Mahout Topics -Machine Learning Fundamentals, Apache Mahout Basics, History of Mahout, Supervised and Unsupervised Learning techniques, Mahout and Hadoop, Introduction to Clustering, Classification. Unit 2: Mahout and Hadoop Topics - Mahout on Apache Hadoop setup, Mahout and Myrrix. Unit 3: Recommendation Engine Topics -Recommendations using Mahout, Introduction to Recommendation systems, Content Based, Mahout Optimizations.
Unit 4: Implementing a recommender and recommendation platform 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. Unit 5: Clustering
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. Unit 6: Classification Topics -Examples, Basics, Predictor variables and Target variables, Common Algorithms, SGD, SVM, Navie Bayes, Random Forests, Training and evaluating a Classifier, Developing a Classifier.
Unit 7: Mahout and Amazon EMR Topics -Mahout on Amazon EMR, Mahout Vs R, Introduction to tools like Weka, Octave, Matlab, SAS. Unit 8: Project Topics -A complete recommendation engine built on application logs and transactions.
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