R Programming for Complete Data Science and Machine Learning

The whole journey of the course is all about R Programming first, then Machine Learning including the concept of Data Science using R programming. Flexibility and ease are the desire of every Data Scientist or machine learning expert, but because of steep learning, student get tired and overwhelmed. The journey of machine learning especially got confused due to Math and Statistics behind! R and Python are interchangeably used in the industry where R has its own strengthen with respect to the statistical packages support on it. In the modern world where the Data is around everywhere study of R with Machine Learning play a tremendous role to clean and filter out the data to make it useful for future predication. In the Job market if you know R programming your chances are high to get job as compare to other languages in the filed of statistics and machine learning.

The Course designed is such a way, that will be useful for all level specially student who are learning and under working on it.

This separate session in Module I is all about R programming including Theoretical and Practical work using R Studio IDE, whereas in Module II, the individually session of supervised and unsupervised machine learning algorithm will be discussed. The discussion of each session based on live and real life example and dataset to make you understand in better way.

The Course has Two Module, in Module-I you will learn:

What is R, Installation, HELLO WORLD!

Variable and Data types

Operators in R

Data Structure

Atomic vector All Operations

List   All Operations

Array   All Operations

Matrices   All Operations

Data Frame   All Operations

Factors   All Operations

Control structures (if statements / Family)

Switch statements

Loops (For, while, repeat)

Jump Statements

Functions & Types

Data Visualization

Advance Data Visualization using ggplot2.

In Module-II you will learn:

Machine Learning Introduction and Dataset

Regression

Linear Regression

Multiple Linear Regression

Polynomial Regression

Support Vector Regression

Classification

Logistic Regression

Support Vector Classification

K Nearest Neighbors Classification

Clustering

Hierarchical Clustering Algorithm

K Means Clustering Algorithm

Association

Apriori Algorithm

Eclat Algorithm

F-P Growth Algorithm

In Final words, the Couse is useful for all skill level, even you do not know about any programming language and Statistics behand it. As we know Learning never end so be focus on skill and technology to make your life comfortable and easy!

Now, I will be very excised to see you in the course.

Regards,

Fahad Hussain