# MACHINE LEARINING USING R PROGRAMMING

### MACHINE LEARNING USING R PROGRAMMING TRAINING

R is a free programming language and software environment for statistical computing and graphics. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. R is an implementation of the S programming language combined with lexical scoping semantics inspired by Scheme.S was created by John Chambers while at Bell Labs. There are some important differences, but much of the code written for S runs unaltered. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team, of which Chambers is a member. R is named partly after the first names of the first two R authors and partly as a play on the name of S. The project was conceived in 1992, with an initial version released in 1994 and a stable beta version in 2000

FUNDAMENTAL OF STATISTICS.

Population and sample
Descriptive and Inferential Statistics
Statistical data analysis
Variables
Sample and Population Distributions
Interquartile range
Central Tendency
Normal Distribution
Skewness.
Boxplot
Five Number Summary
Standard deviation
Standard Error
Emperical Formula
central limit theorem
Estimation
Confidence interval
Hypothesis testing
p-value
Scatterplot and correlation coefficient
Standard Error
Scales of Measurements and Data Types
Data Summarization
Visual Summarization
Numerical Summarization
Outliers & Summary

MODULE 1- INTRODUCTION TO DATA ANALYTICS

Objectives:
This module introduces you to some of the important keywords in R like Business
Analytics, Data and Information. You can also learn how R can play an important role in solving complex analytical problems.
This module tells you what is R and how it is used by the giants like Google, Facebook, etc.
Also, you will learn use of ‘R’ in the industry, this module also helps you compare R with other software
in analytics, install R and its packages.
Topics:
Business Analytics, Data, Information
Understanding Business Analytics and R
Compare R with other software in analytics
Install R
Perform basic operations in R using command line

MODULE 2- INTRODUCTION TO R PROGRAMMING

Starting and quitting R
Basic features of R.
Calculating with R
Named storage
Functions
R is case-sensitive
Listing the objects in the workspace
Vectors
Extracting elements from vectors
Vector arithmetic
Simple patterned vectors
Missing values and other special values
Character vectors Factors
More on extracting elements from vectors
Matrices and arrays
Data frames
Dates and times
NOTE:-
Assignments with Datasets

IMPORT AND EXPORT DATA IN R

MODULE 3- MANAGING DATA FRAMES WITH THE DPLYR PACKAGE

The dplyr Package
Installing the dplyr package
select()
filter()
arrange()
rename()
mutate()
group_by()
%>%
NOTE:-
Assignments

MODULE 4- LOOP FUNCTIONS

Looping on the Command Line
lapply()
sapply()
tapply()
apply()
NOTE-:
Assignments

MODULE 5- DATA MANIPULATION IN R OBJECTIVE

OBJECTIVES

Control Structure Programming with R
The for() loop
The if() statement
The while() loop
The repeat loop, and the break and next statements
Apply
Sapply
Lapply
NOTE:-
Assignments with Datasets

FACTORS

Using Factors
Manipulating Factors
Numeric Factors
Creating Factors from Continuous Variables
Convert the variables in factors or in others.

RESHAPING

Data Modifying
Data Frame Variables
Recoding Variables
The recode Function
Reshaping Data Frames
The reshape Package
NOTE:-
Assignments with Datasets

EXPLORE MANY ALGORITHMS AND MODELS

Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.
Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests. Get ready to do more learning than your machine

ENQUIRY FORM