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    • Support Vector Machines for Beginners Support Vector Machines in R Studio

    SVM for Beginners: Support Vector Machines in R Studio

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    Udemy

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    Lavel

    Beginner

    Mode

    Online

    Fees

    ₹ 449 2899

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    Course and certificate fees

    Fees information
    ₹ 449  ₹2,899
    certificate availability

    Yes

    certificate providing authority

    Udemy

    The syllabus

    Setting up R Studio and R Crash Course

    • Installing R and R Studio
    • Course Resources
    • Basics of R and R Studio
    • This is a milestone!
    • Packages in R
    • Inputting data part 1: Inbuilt datasets of R
    • Inputting data part 2: Manual data entry
    • Inputting data part 3: Importing from CSV or Text files
    • Creating Barplots in R
    • Creating Histograms in R

    Machine Learning Basics

    • Introduction to Machine Learning
    • Building a Machine Learning Model

    Maximum Margin Classifier

    • Course flow
    • The Concept of a Hyperplane
    • Maximum Margin Classifier
    • Limitations of Maximum Margin Classifier

    Support Vector Classifier

    • Support Vector classifiers
    • Limitations of Support Vector Classifiers
    • Quiz

    Support Vector Machines

    • Kernel Based Support Vector Machines
    • Quiz

    Creating Support Vector Machine Model in R

    • The Data set for the Classification problem
    • Importing Data into R
    • Test-Train Split
    • More about test-train split
    • Classification SVM model using Linear Kernel
    • Hyperparameter Tuning for Linear Kernel
    • Polynomial Kernel with Hyperparameter Tuning
    • Radial Kernel with Hyperparameter Tuning
    • Quiz
    • The Data set for the Regression problem
    • SVM based Regression Model in R

    Appendix 1: Preprocessing and Preparing Data before making any model

    • Gathering Business Knowledge
    • Data Exploration
    • The Data and the Data Dictionary
    • Importing the dataset into R
    • Univariate Analysis and EDD
    • EDD in R
    • Outlier Treatment
    • Outlier Treatment in R
    • Missing Value imputation
    • Missing Value imputation in R
    • Seasonality in Data
    • Bi-variate Analysis and Variable Transformation
    • Variable transformation in R
    • Non Usable Variables
    • Dummy variable creation: Handling qualitative data
    • Dummy variable creation in R
    • Correlation Matrix and cause-effect relationship
    • Correlation Matrix in R
    • Quiz

    Bonus Section

    • The final milestone!
    • Bonus Lecture

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