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    • Decision Trees Random Forests AdaBoost and XGBoost in Python

    Decision Trees, Random Forests, AdaBoost & XGBoost in Python

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    Udemy

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    Mode

    Online

    Fees

    ₹ 3499

    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
    ₹ 3,499
    certificate availability

    Yes

    certificate providing authority

    Udemy

    The syllabus

    Introduction

    • Welcome to the Course!
    • Course Resources

    Setting up Python and Python Crash Course

    • Installing Python and Anaconda
    • This is a milestone!
    • Opening Jupyter Notebook
    • Introduction to Jupyter
    • Arithmetic operators in Python: Python Basics
    • Strings in Python: Python Basics
    • Lists, Tuples and Directories: Python Basics
    • Working with Numpy Library of Python
    • Working with Pandas Library of Python
    • Working with Seaborn Library of Python

    Machine Learning Basics

    • Introduction to Machine Learning
    • Building a Machine Learning 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 Data in Python
    • Missing value treatment in Python
    • Dummy Variable creation in Python
    • Dependent- Independent Data split in Python
    • Test-Train split in Python
    • More about test-train split
    • Creating Decision tree in Python
    • Evaluating model performance in Python
    • Plotting decision tree in Python
    • Pruning a tree
    • Pruning a tree in Python

    Simple Classification Tree

    • Classification tree
    • The Data set for Classification problem
    • Classification tree in Python : Preprocessing
    • Classification tree in Python : Training
    • Advantages and Disadvantages of Decision Trees

    Ensemble technique 1 - Bagging

    • Ensemble technique 1 - Bagging
    • Ensemble technique 1 - Bagging in Python

    Ensemble technique 2 - Random Forests

    • Ensemble technique 2 - Random Forests
    • Ensemble technique 2 - Random Forests in Python
    • Using Grid Search in Python

    Ensemble technique 3 - Boosting

    • Boosting
    • Quiz
    • Ensemble technique 3a - Boosting in Python
    • Ensemble technique 3b - AdaBoost in Python
    • Ensemble technique 3c - XGBoost in Python
    • Quiz
    • Quiz

    Add-on 1: Preprocessing and Preparing Data before making ML model

    • Gathering Business Knowledge
    • Data Exploration
    • The Dataset and the Data Dictionary
    • Importing Data in Python
    • Univariate analysis and EDD
    • EDD in Python
    • Outlier Treatment
    • Outlier Treatment in Python
    • Missing Value Imputation
    • Missing Value Imputation in Python
    • Seasonality in Data
    • Bi-variate analysis and Variable transformation
    • Variable transformation and deletion in Python
    • Non-usable variables
    • Dummy variable creation: Handling qualitative data
    • Dummy variable creation in Python
    • Correlation Analysis
    • Correlation Analysis in Python
    • Quiz

    Conclusion

    • The final milestone!
    • Bonus Lecture

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