Machine Learning

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Machine Learning

    Module 1: Introduction to Data Analytics

    • 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

    • 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

    • 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

    • Reading Data
    • Writing Data
    • Basic SQL queries in R
    • Web Scraping

    Module 5: Exploratory data Analysis

    • Box plot
    • Histogram
    • Pareto charts
    • Pie graph
    • Line chart
    • Scatterplot
    • Developing Graphs

    Module 6: Basics of Statistics & Linear & Logistic Regression

    • Basics of Statistics
    • Inferencial statistics
    • Probability
    • Hypothesis
    • Standard deviation
    • Outliers
    • Correlation
    • Linear & Logistic Regression

    Module 7: Data Mining: Clustering techniques, Regression & Classification

    • Introduction to Data Mining
    • Understanding Machine Learning
    • Supervised and Unsupervised Machine Learning Algorithms
    • K- means clustering

    Module 8: Anova & Sentiment Analysis

    • Anova
    • Sentiment Analysis

    Module 9: Data Mining: Decision Trees and Random Forest

    • Decision Tree
    • Concepts of Random Forest
    • Working of Random Forest
    • Features of Random Forest

    Module 10: Project work