Machine Learning Using R

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Machine Learning using R

    Module 1: Introduction to Data Analytics

      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

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

        Topics

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

        Objectives:

        • 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

        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

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

          Topics

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

          Module 5: Loops and Date

          • 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

          Topics

          • 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)

          Module 6: Exploratory Data Analysis

            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
            • Doughnut graph
            • Pie graph
            • Line chart
            • Scatterplot

            Module 7: Statistics and Machine Learning – Regression and Classification

              Objectives:

              • 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

              Statistics Topics

              • 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

              Machine Learning Topics

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

              Module 8: Project work:

              • 3 Real-time projects