Description

R programming is a language and interface for statistical computation and graphic  representation and reporting. R is one of the most user-friendly programs with a  simple interface. It has an open source i.e. it is a free software. It is widely used for  data mining and handling Big Data. There are numerous packages and functions which  makes data cleaning, data pre-processing, data exploration, data interpretation,  predictions and forecasting simple.

Machine Learning involves the ability to develop models or programs which can  automatically learn from experience and past data and make the decisions without  human interaction. In easy words, it is basically teaching your machine to learn from  the past results, make certain adjustments to it and then finally make decisions. A  remarkably interesting example

People You May Know: Machine learning works on a simple concept: understanding  with experiences. Facebook continuously notices the friends that you connect with, the  profiles that you visit very often, your interests, workplace, or a group that you share  with someone etc. On the basis of continuous learning, a list of Facebook users is  suggested that you can become friends with.

“A breakthrough in machine learning would be worth ten Microsofts.” 

— Bill Gates, Former Chairman, Microsoft 

Points to Cover 

R PROGRAMMING 

• Introductions to R GUI, R Studio

• R packages

• Base R functions (mathematical, logical, statistical)

• Objects- Vectors, Factors, Matrices, Arrays, Dataframes & Lists

• Importing and Exporting Data in R

• Looping in R

• IF Conditions,

• User defined functions

• Data manipulation using dplyr Package

• Date, Time Functions

• Missing value and Outlier Treatment

• tidyr Package

• stringr Package

• Advanced visualization using GGPlot 2

MACHINE LEARNING 

• Course Overview

• ML Algorithms

• Handling Unstructured Data • Data Preprocessing

• Linear and Logistics Regression • K-Nearest neighbors

• Regression and Classification Trees • Decision Trees & Random Forests • Support Vector Machines • K-Means Clustering

• Agglomerative Clustering

• Model Validation