USING "R"


Machine Learning using

Course Overview


Machine learning is a branch in computer science that studies the design of algorithms that can learn. Typical machine learning tasks are concept learning, function learning or “predictive modeling”,clustering and finding predictive patterns. These tasks are learned through available data that were observed through experiences or instructions, for example. Machine learning hopes that including the experience into its tasks will eventually improve the learning. The ultimate goal is to improve the learning in such a way that it becomes automatic, so that humans like ourselves don’t need to interfere anymore. This small tutorial is meant to introduce you to the basics of machine learning in R: more specifically, it will show you how to use R to work with the well-known machine learning. 

Expectations and Goals:

The course aims at providing an accessible introduction to various machine learning methods and applications in R.The core of the courses focuses on unsupervised and supervised methods. The course contains numerous exercises to provide numerous opportunities to apply the newly acquired material. To help students to build a project self-dependently.. 

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R Recursive Function

R Infix Operator

R switch () Function

R data structures
R Vector
R Matrix
R Lists
R Data Frame
R Factors

Module 3:

Graphs & charts
R Bar Plot
R Histograms
R Pie Chart
R Box Plot
R Strip Chart
R Plot Color

Module 4:

More on plotting in R
R Plot Function
R Multiple Plots
Saving a Plot in R
R 3D Plot

Module 5:

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Need some knowledge about any programming language and need some concepts about statistics.

About R language
R Studio
Reserved Words
Variables and Constants
R Operators
Operator Precedence
R Programming if...else
R if else () Function
R for Loop
R while Loop
R break and next Statement
R repeat loop

Topics Covered:

Module 1:

Module 2:

R functions
R Functions
R Return Value from Function

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Machine learning concepts
Introduction to Machine Learning
Data upload in R system
Data Processing

Module 6:

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Concepts on various ml algorithms
Regression Analysis:
Simple Linear regression
Logistic regression
Support Vector machine
Decision Tree Regression
Decision Tree Classification
Random Forest Classification
Naive Bayes

Module 7:

Implementation or practical of various machine learning algorithms on cloud environment

Module 8:

Project work and documentation

Module 8:

Thank You

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