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    • Support Vector Machines in Python Support Vector Machines Concepts and Code

    Support Vector Machines in Python: SVM Concepts & Code

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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

    Setting up Python and Python Crash Course

    • Installing Python and Anaconda
    • Course Resources
    • Opening Jupyter Notebook
    • This is a milestone!
    • 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

    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 Python

    • Regression and Classification Models
    • The Data set for the Regression problem
    • Importing data for regression model
    • Missing value treatment
    • Dummy Variable creation
    • X-y Split
    • Test-Train Split
    • More about test-train split
    • Standardizing the data
    • SVM based Regression Model in Python
    • The Data set for the Classification problem
    • Classification model - Preprocessing
    • Classification model - Standardizing the data
    • SVM Based classification model
    • Hyper Parameter Tuning
    • Polynomial Kernel with Hyperparameter Tuning
    • Radial Kernel with Hyperparameter Tuning

    Appendix 1: Data Preprocessing

    • 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

    Bonus Section

    • The final milestone!
    • Bonus Lecture

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