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    Machine Learning and AI: Support Vector Machines in Python

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    Udemy

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    Mode

    Online

    Fees

    ₹ 599 3099

    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
    ₹ 599  ₹3,099
    certificate availability

    Yes

    certificate providing authority

    Udemy

    The syllabus

    Welcome

    • Introduction
    • Course Objectives
    • Course Outline
    • Where to get the code and data

    Beginner's Corner

    • Beginner's Corner: Section Introduction
    • Image Classification with SVMs
    • Spam Detection with SVMs
    • Medical Diagnosis with SVMs
    • Regression with SVMs
    • Cross-Validation
    • How do you get the data? How do you process the data?
    • Suggestion Box

    Review of Linear Classifiers

    • Basic Geometry
    • Normal Vectors
    • Logistic Regression Review
    • Loss Function and Regularization
    • Prediction Confidence
    • Nonlinear Problems
    • Linear Classifiers Section Conclusion

    Linear SVM

    • Linear SVM Section Introduction and Outline
    • Linear SVM Problem Setup and Definitions
    • Margins
    • Linear SVM Objective
    • Linear and Quadratic Programming
    • Slack Variables
    • Hinge Loss (and its Relationship to Logistic Regression)
    • Linear SVM with Gradient Descent
    • Linear SVM with Gradient Descent (Code)
    • Linear SVM Section Summary

    Duality

    • Duality Section Introduction
    • Duality and Lagrangians (part 1)
    • Lagrangian Duality (part 2)
    • Relationship to Linear Programming
    • Predictions and Support Vectors
    • Why Transform Primal to Dual?
    • Duality Section Conclusion

    Kernel Methods

    • Kernel Methods Section Introduction
    • The Kernel Trick
    • Polynomial Kernel
    • Gaussian Kernel
    • Using the Gaussian Kernel
    • Why does the Gaussian Kernel correspond to infinite-dimensional features?
    • Other Kernels
    • Mercer's Condition
    • Kernel Methods Section Summary

    Implementations and Extensions

    • Dual with Slack Variables
    • Simple Approaches to Implementation
    • SVM with Projected Gradient Descent Code
    • Kernel SVM Gradient Descent with Primal (Theory)
    • Kernel SVM Gradient Descent with Primal (Code)
    • SMO (Sequential Minimal Optimization)
    • Support Vector Regression
    • Multiclass Classification

    Neural Networks (Beginner's Corner 2)

    • Neural Networks Section Introduction
    • RBF Networks
    • RBF Approximations
    • What Happened to Infinite Dimensionality?
    • Build Your Own RBF Network
    • Relationship to Deep Learning Neural Networks
    • Neural Network-SVM Mashup
    • Neural Networks Section Conclusion

    Setting Up Your Environment (FAQ by Student Request)

    • Anaconda Environment Setup
    • How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

    Extra Help With Python Coding for Beginners (FAQ by Student Request)

    • How to Code by Yourself (part 1)
    • How to Code by Yourself (part 2)
    • Proof that using Jupyter Notebook is the same as not using it
    • Python 2 vs Python 3

    Effective Learning Strategies for Machine Learning (FAQ by Student Request)

    • How to Succeed in this Course (Long Version)
    • Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
    • Machine Learning and AI Prerequisite Roadmap (pt 1)
    • Machine Learning and AI Prerequisite Roadmap (pt 2)

    Appendix / FAQ Finale

    • What is the Appendix?
    • BONUS Lecture

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