The following topics are covered in “Machine Learning”

Foundation:

Machine Learning Introduction: Supervised and Unsupervised Learning

• Linear Regression Theory
• Linear Regression Programming with R
• Working on Case Study

Multiple Linear Regression

• Theory behind multiple linear regression
• Multiple Linear Regression with R
• Working on Case Study

Decision Tree:

• Theory Behind Decision Tree
• Decision Tree with R
• Working on Case Study

Naive Bayes:

• Theory behind Naïve Bayes classifiers
• Naive Bayes Classifiers with R
• Working on Case Study

Support Vector Machines:

• Theory behind Support Vector Machines
• Support vector machines with R
• Improving the performance with Kernals
• Working on Case Study

Association Rule:

• Theory behind Association Rule
• Working on Case Studies

Expert:

Neural Net:

• Artificial Neural Network
• Connection Weights in Neural Network
• Generating Neural Network with R
• Improving Neural Network Accuracy with Hidden Layers
• Working on Case

Random Forest:

• Theory behind Random Forest
• Random Forest with R
• Improving performance of Random Forest
• Working on Case Study

Recommendation Engine:

• Theory behind Recommendation Engines
• Working on Case Study with R

Dimension Reduction:

• Theory behind Recommendation Engine
• Working on Case Studies