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    Decision Trees, Random Forests, Bagging & XGBoost: R Studio

    BY
    Udemy

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    Mode

    Online

    Fees

    ₹ 3699

    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
    ₹ 3,699
    certificate availability

    Yes

    certificate providing authority

    Udemy

    The syllabus

    Introduction

    • Welcome to the Course!
    • Course Resources

    Setting up R Studio and R Crash Course

    • Installing R and R studio
    • This is a milestone!
    • Basics of R and R studio
    • 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, Key concepts and Examples
    • Steps in building an ML model

    Simple Decision trees

    • Basics of Decision Trees
    • Understanding a Regression Tree
    • The stopping criteria for controlling tree growth
    • The Data set for the Course
    • Importing the Data set into R
    • Splitting Data into Test and Train Set in R
    • More about test-train split
    • Building a Regression Tree in R
    • Pruning a tree
    • Pruning a Tree in R

    Simple Classification Tree

    • Classification Trees
    • The Data set for Classification problem
    • Building a classification Tree in R
    • Advantages and Disadvantages of Decision Trees

    Ensemble technique 1 - Bagging

    • Bagging
    • Bagging in R

    Ensemble technique 2 - Random Forest

    • Random Forest technique
    • Random Forest in R

    Ensemble technique 3 - Boosting

    • Boosting techniques
    • Quiz
    • Gradient Boosting in R
    • AdaBoosting in R
    • XGBoosting in R
    • Quiz

    Add-on 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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