Introduction to R

Describe why R is Used?

Implement R programing concepts

Learn Data Import techniques

Analyze the processing of the Data

Observation and Experiments

Sampling Methods

Quantitative Variables

Skewness,Modality and Measures of Center

Variance, Standard Deviation, Interquartile Range

Probability Rules

Disjoint,Non Disjoint events, Independence

Conditional Probability

Probability Distributions

Understand Machine Learning

Use Cases Walkthrough

Machine Learning Techniques

Describe Clustering

Analyze Clustering Scenarios using Clustering Algorithms

Learn TF-IDF and cosine Similarity

Understand Supervised Learning Technique

Classification

Recommendation

Learn Decision Tree Classifier

Implement how various Decision Tree algorithms work.

Implement Application of Techniques on a smaller datasets for better understanding using R

Understand Unsupervised Learning Technique

Understand the implementation of Random Forest Classifier

Understand the implementation of Na-ve Bayer’s Classifier

Apply both techniques on smaller datasets using R

Understand Association Rule Mining

Understand the need for R integration with Hadoop

Learn the ways to integrate R and Hadoop

Understand the usage of RHadoop package

Perform R integration with Hadoop and Run MapReduce examples

Understand Mahout

Gain insight on implementing Machine Learning with Mahout

Understand Learning, Classification and Clustering techniques with Mahout

Implement Recommendation technique and Frequent Pattern Mining in Mahout

Understand Mahout Algorithms and Parallel proicessing

Learn Advanced techniques in R

Implement Parallel Random Forest

Understand Data Visualization