Apache Mahout Training Course Content

1: Introduction to Machine Learning and Apache Mahout
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
Mahout on Apache Hadoop setup
Mahout and Myrrix

3: Recommendation Engine
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
User based recommendation
User Neighborhood
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)
Recommenders in Production

5: Clustering
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
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
Mahout on Amazon EMR
Mahout Vs R
Introduction to tools like Weka
Octave, Matlab, SAS

Getting the right solution based on the criteria curated by SoftPro9 Team