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    Support Vector Machine A-Z: Support Vector Machine Python ©

    BY
    Udemy

    Brochure Enquire
    Lavel

    Beginner

    Mode

    Online

    Fees

    ₹ 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
    ₹ 2,899
    certificate availability

    Yes

    certificate providing authority

    Udemy

    The syllabus

    Introduction to course

    • Introduction to the Course
    • Introduction To Instructor
    • Why Machine Learning
    • Why Support Vector Machine
    • Course Overview
    • Please give us some feedback and Review
    • Link to the Python codes for the projects and the data

    Introduction to Machine Learning

    • Link to the Python codes for the projects and the data
    • Introduction to Machine Learning, Learning Process and Supervised Learning
    • UnSupervised Learning and Reinforcement Learning
    • History and Future of Machine Learning
    • Dataset, Label and Features
    • Training Data,Testing Data and Outliers
    • Model
    • Model (Difference between Classification and Regression)
    • Model (Function,Parameters,Hyperparameters)
    • Training a model,Cost,Error,Loss,Risk,Accuracy
    • Optimization
    • Overfitting,Underfitting,Just RightOptimum (Part 1)
    • Overfitting,Underfitting,Just RightOptimum (Part 2)
    • Validation and Cross Validation,Generalization,Data Snooping,Validation Set
    • Probability Distributions and Curse of Dimensionlity
    • Small Sample Size problems,One Shot Learning
    • Importance of Data in Machine Learning,Data Encoding and Preprocessing
    • General Flow of a typical Machine Learning Project

    Introduction to Python

    • Link to the Python codes for the projects and the data
    • Introduction to Python
    • Introduction to IDE,Hello World
    • Introduction to Data Type, Numbers
    • Variable and Operators (Numbers)
    • Variables and Operators (Rational Operators and Functions)
    • Variables and Operators (String)
    • Variables and Operators (String and print Statement)
    • Lists(Indexing,Slicing-Built in Lists Functions)
    • Lists(Copying a List)
    • Tuples(Indexing,Slicing,Built in Tuple Functions)
    • Set(initialize,Built in Set Functions)
    • Dictionary
    • Logical Operator,Decision Making,For Loops,While Loops,Functions
    • Logical Operator,Decision Making,For Loops,While Loops,List Comprehension
    • Functions
    • Calculator Project

    Support Vector Machine

    • Link to the Python codes for the projects and the data
    • Introduction SVM
    • Linear Discriminants
    • Linear Discriminants higher spaces
    • Linear Discriminants Decision Boundary
    • Generalized Linear Model
    • Feature Transformation
    • Max Margin Linear Discriminant
    • Hard Margin Vs Soft Margin
    • Confidence
    • Multiclass Extension
    • SVM Vs Logistic Regression Sparsity
    • SVM Optimization
    • SVM Langrangian Dual
    • Kernels
    • Python Packages & Titanic DataSet
    • Using Numpy, Pandas and Matplotlib (Part 1)
    • Using Numpy, Pandas and Matplotlib (Part 2)
    • Using Numpy, Pandas and Matplotlib (Part 3)
    • Using Numpy, Pandas and Matplotlib (Part 4)
    • Using Numpy, Pandas and Matplotlib (Part 5)
    • Using Numpy, Pandas and Matplotlib (Part 6)
    • DataSet Preprocessing
    • SVM with Sklearn
    • SVM without Sklearn (Part 1)
    • SVM without Sklearn (Part 2)

    Optional SVM Section

    • Link to the Python codes for the projects and the data
    • Optional SVM Optimization (Part 1)
    • Optional SVM Optimization (Part 2)
    • Optional SVM Optimization (Part 3)
    • Optional SVM Optimization (Part 4)
    • Optional SVM Optimization (Part 5)
    • Optional SVM Optimization (Part 6)

    Bonus Lecture

    • Link to the Python codes for the projects and the data
    • THANK YOU Bonus Video

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