Syllabus

1. Fundamentals of R Programming
Capabilities of R
Datatypes in R
Data Structures in R
Data Inputting in R
Data Manipulation in R
Functions and Programming in R
Data Visualization in R

2. Introduction to Machine Learning
3. Introduction to Basic Statistics
4. Exploratory Data Analysis using R
5. Modelling Concepts and Notations

6. Regression
I. Simple Regression
II. Multilinear Regression
III. Polynomial Regression
7. Classification
kNN
LDA
QDA
8. Logistic Regression
Support Vector Machines
Model Assessment and Selection

9. Trees & Ensemble Models
Bagging
Boosting
Random Forest

10. Clustering Techniques
k-Means
Hierarchical Clustering
PCA

11. Capstone Project