- Introduction
- Course Objectives
- Course Outline
- Where to get the code and data
Online
₹ 1,299
Quick facts
particular | details | |
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course and certificate fees
Fees information
₹ 1,299
certificate availability
Yes
certificate providing authority
Udemy
The syllabus
Welcome
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