# Machine Learning with R

### Self-paced Online Course

## About R

A programming language for statistical computing, R is one most widely used software environments for computational statistics, data science and visualisation. Millions of analysts and data scientists use R for problems ranging from quantitative finance and computational biology to market research and behavioural studies. Though a freeware, recent surveys show that the adoption of R is fast outpacing legacy, proprietary data analysis software, which continue to lag behind it in features and functionalities.

## Who is this course for?

This is a course meant to introduce you to Machine Learning techniques such as Decision tree, Logistic Regression, Naive Bayes, Clustering, etc as well as a fundamental introduction to R programming. If you are unfamiliar with machine learning or you want to implement in your area of work, then this course is for you. The course provides elaborate explanation of the concepts, and other necessary techniques in R for beginners.

## Why this course?

Machine Learning is a method of data analysis that automates analytical model building.
It is also a branch in computer science. The tasks in machine learning viz logistic regression,
clustering, decision tree are widely used in different field of studies.
It is equally important and useful to know the theories behind machine learning,
to know where and how to apply. This also helps one to better interpret the results.
This course on machine learning teaches,

• The theories and concepts behind the techniques.

• The way these techniques need to be applied in R.

• Interpreting and drawing conclusions from the outcomes.

## How is this course taught?

We don’t believe in learning theories only. To gain confidence one have to apply the techniques in practical scenario. This course is taught through an unique simulated interface, wherein you will be taught the concepts and theories, the R-commands for the techniques, how they are to be executed in R and the interpretation of the results that R produces. Keep R running in another window, follow the steps as training demonstrates and you will easily get the skills to be able to apply them in your field of work.

# Coverage

### Introduction to R

**• Background and Resources**

History behind R and online resources for R.

**• Installing R**

Installing R in windows.

**• R Console**

R window to edit and execute R commands.

**• R Commander**

Installation and Activation of R commander, Interface of R commander, Dataset:Activate, Edit and View, Get Help on Activate dataset, Analysis on Activated dataset.

**• Commands and Syntax**

R Commands and R Syntax.

**• Packages and Libraries**

Install and load a package in R.

**• Help in R**

Getting help about R commands.

**• Workspace in R**

Save and load R file in workspace.

### Decision Tree

**• White Board: Decision Tree**

Introduction to Decision tree, concepts and theory.

**• Do it yourself: Build a C5 decision tree**

Follow the step by step online simulation to build a classification decision tree using C5.

**• Do it yourself: Build a CART decision tree**

Follow the step by step online simulation to build a regression tree using CART.

### Logistic Regression

**• White Board: Logistic Regression**

Introduction to Logistic regression, concepts and theory.

**• Do it yourself: Build a Logistic Regression Model**

Follow the step by step online simulation to build a Logistic regression model.

### Association Analysis

**• White Board: Association Analysis**

Introduction to Association Analysis, concepts and theory.

**• Do it yourself: Build a Apriori Model**

Follow the step by step online simulation to build a Apriori Model.

### Data Structures

**• White Board: Introduction To Data Types - 1**

**• White Board: Introduction To Data Types - 2**

**• White Board: Introduction To Data Types - 3**

**• Introduction To Data Structures**

Why data structures. Types of data structures in R.

**• Vectors**

Types of Vectors and their creation procedures. Assigning created Vector to an object. Basic vector operations. Operations between vectors.

**• Matrices**

Creating a matrix.Extracting elements rows or columns from a matrix. Combining two matrices, Basic matrix operations.

**• Arrays**

Creating an Array. Finding type and dimension of Array.

**• Lists**

Creating a List. Extracting a specific component from a list. Extracting a component from a sublist.

**• Factors**

Creating a factor. Unordered and ordered factors.

**• Dataframes**

Creating a Dataframe. Examining different parts of a dataframe. Editing and saving a dataframe.

**• Importing and Exporting data**

Import from and export to CSV, SPSS, text file and Excel.

**• Data types**

Numerical, nominal and ordinal data types. Modifying data types.

### Neural Network

**• White Board: Neural Network**

Introduction to Neural Network, concepts and theory.

**• Do it yourself: Build a Neural Network Model**

Follow the step by step online simulation to build a Neural Network Model.

### Graphical Analysis

**• Creating a Simple Graph**

Using plot() command.

**• Modifying the Points and Lines of a Graph**

Using type, pch, font, cex, lty, lwd, col arguments in plot() command.

**• Modifying Title and Subtitle of a Graph**

Using main, sub, col.main, col.sub, cex.main, cex.sub, font.main, font.sub arguments in plot() command.

### Support Vector Machine

**• White Board: Support Vector Machine**

Introduction to Support Vector Machine, concepts and theory.

**• Do it yourself: Build a Support Vector Machine Model**

Follow the step by step online simulation to build a Support Vector Machine Model.

### Naive Bayes

**• White Board:Naive Bayes**

Introduction to Naive Bayes, concepts and theory.

**• Do it yourself: Build a Naive Bayes Model.**

Follow the step by step online simulation to build a Naive Bayes Model.

### Clustering

**• White Board:Clustering**

Introduction to Clustering, concepts and theory.

**• Do it yourself: Build a Hierarchical Clustering Model.**

Follow the step by step online simulation to build a Hierarchical Clustering Model.

**• Do it yourself: Build a K-Means Clustering Model.**

Follow the step by step online simulation to build a K-Means Clustering Model.