This Machine Learning with Mahout certification training course designed to provide a blend of Machine learning and Big Data and where Mahout fits in the Hadoop Ecosystem.
After the completion of Apache Mahout Course, you should be able to:
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, User based, Nearest N Users, Threshold, 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 built on application logs and transactions
Once you are successfully through the project (reviewed by our expert team), you will be awarded with the Machine Learning with Mahout Expert Certificate.
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