Machine Learning using R Training in jaya nagar
Our Machine Learning using R Training in jaya nagar aims to teach the complete Data Warehousing Concepts in an easier way. We are the Best Machine Learning using R Training Institute in jaya nagar in-terms of a syllabus and expert teaching. We are covering almost all the transformations which are required for companies in Informatica.
Machine Learning using R Training Syllabus
Total Duration: 42:00:00 Hrs
Module 1- Introduction to Data Analytics
Duration: 04:00:00 Hrs
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
Learn the use of IDE R Studio
Use the ‘R help’ feature in R
Module 2- Introduction to R programming
Duration: 03:00:00 Hrs
Objectives:
This module starts from the basics of R programming like datatypes and functions.
In this module, we present a scenario and let you think about the options to resolve it, such as which datatype should one to store the variable or which R function that can help you in this scenario.
You will also learn how to apply the ‘join’ function in SQL.
Topics:
Variables in R
Scalars
Vectors
Matrices
List
Data frames
Using c, Cbind, Rbind, attach and detach functions in R
Factors
Module 3- Data Manipulation in R
Duration: 04:00:00 Hrs
Objectives:
In this module, we start with a sample of a dirty data set and perform Data Cleaning on it, resulting in a data set, which is ready for any analysis.
Thus using and exploring the popular functions required to clean data in R.
Topics:
Data sorting
Find and remove duplicates record
Cleaning data
Recoding data
Merging data
Slicing of Data
Merging Data
Apply functions
Module 4- Data Import techniques in R
Duration: 04:00:00 Hrs
Objectives:
This module tells you about the versatility and robustness of R which can take-up data in a variety of formats, be it from a csv file to the data scraped from a website.
This module teaches you various data importing techniques in R.
Topics:
Reading Data
Writing Data
Basic SQL queries in R
Web Scraping
Module 5- Exploratory Data Analysis
Duration: 05:00:00 Hrs
Objectives:
In this module, you will learn that exploratory data analysis is an important step in the analysis.
EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis. You will also learn about the various tasks involved in a typical EDA process.
Topics:
Box plot
Histogram
Pareto charts
Pie graph
Line chart
Scatterplot
Developing Graphs
Module 6- Basics of Statistics & Linear & Logistic Regression
Duration : 05:00:00 Hrs
Objectives:
This module touches the base of Descriptive and Inferential Statistics and Probabilities & ‘Regression Techniques’.
Linear and logistic regression is explained from the basics with the examples and it is implemented in R using two case studies dedicated to each type of Regression discussed.
Topics:
Basics of Statistics
Inferential statistics
Probability
Hypothesis
Standard deviation
Outliers
Correlation
Linear & Logistic Regression
Module 7- Data Mining: Clustering techniques, Regression & Classification
Duration : 04:00:00 Hrs
Objectives:
Linear and logistic regression is explained from the basics with the examples and it is implemented in R using two case studies dedicated to each type of Regression discussed.
The two Machine Learning types are Supervised Learning and Unsupervised Learning and the difference between the two types.
We will also discuss the process involved in ‘K-means Clustering’, the various statistical measures you need to know to implement it in this module.
Topics:
Introduction to Data Mining
Understanding Machine Learning
Supervised and Unsupervised Machine Learning Algorithms
K- means clustering
Module 8- Anova & Sentiment Analysis
Duration : 02:00:00 Hrs
Objectives:
This module tells you about the Analysis of Variance (Anova) Technique.
The algorithm and various aspects of Anova have been discussed in this module
Additionally, this module also deals with Sentiment Analysis and how we can fetch, extract and mine live data from Twitter to find out the sentiment of the tweets.
Topics:
Anova
Sentiment Analysis
Module 9- Data Mining: Decision Trees and Random Forest
Duration: 03:00:00 Hrs
Objectives:
This module covers the concepts of Decision Trees and Random Forest.
The algorithm of Random Forests is discussed in a step-wise approach and explained with real-life examples.
Topics:
Decision Tree
Concepts of Random Forest
Working of Random Forest
Features of Random Forest
Module 10- Project work
Duration: 10:00:00 Hrs
2 Real-time projects