MACHINE LEARINING USING R PROGRAMMING

ABOUT US

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
    Intelligence, 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
    Recording your work
    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
    Business Scenerio/Group Discussion.



MODULE 4- LOOP FUNCTIONS

    Looping on the Command Line
    lapply()
    sapply()
    tapply()
    apply()
    NOTE-:
    Assignments
    Business Scenerio/Group Discussion.



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


FOR MORE SYLLABS DOWNLOAD PDF FILE

ENQUIRY FORM