Machine Learning Using R Training in Chennai

Syllabus of Machine Learning using R Course in Chennai

• 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

• 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.
• Will cover the basics of data types and conversion between them

• Variables in R
• Scalars
• Vectors
• Matrices
• List
• Data frames
• Using c, Cbind, Rbind, attach and detach functions in R

• In this module, we start with a sample of a messy dataset and perform Data Cleaning on it, resulting in a dataset, which is ready for any analysis.
• Before starting any analysis data cleaning is the first process to do

• Data sorting
• Find and remove duplicates record
• Cleaning data
• Recoding data
• Merging data
• Slicing of Data
• Merging Data
• Apply functions

• 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
• This module teaches you various data importing techniques in R.

• Reading and writing flat files – text,csv,Excel
• Reading Techniques in R
• How to connect mysql with R
• Saving and loading R format

• This module tells you concepts of loop technique. From this we can automate anything in R and give the enhancement to your code
• Dates will comes in any format, so that converting the dates, difference and slicing date information is most important in real time

• For loop
• While loop
• If loop
• Break and continue
• How to convert to date format
• How to slice dates information (Year, Quarters, Months, Days, Hours etc..,)
• How to get difference between two days (Year, Quarters, Months, Days, Hours)

• 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.

• Box plot
• Histogram
• Doughnut graph
• Pie graph
• Line chart
• Scatterplot

• This module touches the base of Descriptive and Inferential Statistics and machine learning concepts
• All algorithm explained by – what is the mathematics behind for that, how it is getting different from other algorithm, which scenario want to apply, how to interpret with R and what insights getting from final result

• Probability
• Hypothesis Testing – Null Hypothesis and Alternate Hypothesis
• Standard deviation and Variance
• Outliers – Detection and Replacement method
• Correlation
• T test- Unpaired and Paired
• Chi square
• Anova

• Linear Regression
• Multiple Regression
• Logisitic Regression
• Naïve Bayes Classifier
• Variable selection on model
• K means Clustering
• Decision Tree
• SVM
• Time series forecasting

• 3 Real-time projects