This course will prepare you to:

• Explain the architecture of the Machine Learning with Mahout Certification component
• Configure and use new functionalities in Machine Learning with Mahout Certification
• Use the standard Machine Learning with Mahout Certification Sub Modules.
• Explain the Machine Learning with Mahout Certification Controlling Configuration and Customization option.

• 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

Practice Test and Interview Questions