Machine Learning and AI: Support Vector Machines in Python

  • Welcome
  • Introduction
Introduction
02:21
Course Objectives
04:55
Course Outline
05:50
Where to get the code and data
05:49
Beginner's Corner: Section Introduction
05:19
Image Classification with SVMs
06:01
Spam Detection with SVMs
11:48
Medical Diagnosis with SVMs
05:16
Regression with SVMs
05:36
Cross-Validation
07:21
How do you get the data? How do you process the data?
05:22
Basic Geometry
10:52
Normal Vectors
03:42
Logistic Regression Review
09:46
Loss Function and Regularization
04:10
Prediction Confidence
07:26
Nonlinear Problems
09:59
Linear Classifiers Section Conclusion
04:26
Suggestion Box
03:10
Linear SVM Section Introduction and Outline
03:19
Linear SVM Problem Setup and Definitions
04:31
Margins
08:52
Linear SVM Objective
11:01
Linear and Quadratic Programming
12:32
Slack Variables
07:26
Hinge Loss (and its Relationship to Logistic Regression)
06:23
Linear SVM with Gradient Descent
03:11
Linear SVM with Gradient Descent (Code)
05:07
Linear SVM Section Summary
04:15
Duality Section Introduction
03:44
Duality and Lagrangians (part 1)
13:02
Lagrangian Duality (part 2)
07:09
Relationship to Linear Programming
04:20
Predictions and Support Vectors
09:17
Why Transform Primal to Dual?
03:27
Duality Section Conclusion
02:55
Kernel Methods Section Introduction
03:48
The Kernel Trick
08:12
Polynomial Kernel
06:07
Gaussian Kernel
05:14
Using the Gaussian Kernel
07:10
Why does the Gaussian Kernel correspond to infinite-dimensional features?
04:40
Other Kernels
07:05
Mercer's Condition
06:25
Kernel Methods Section Summary
02:42
Dual with Slack Variables
10:41
Simple Approaches to Implementation
06:26
SVM with Projected Gradient Descent Code
08:20
Kernel SVM Gradient Descent with Primal (Theory)
04:31
Kernel SVM Gradient Descent with Primal (Code)
04:56
SMO (Sequential Minimal Optimization)
09:33
Support Vector Regression
05:27
Multiclass Classification
04:35
Neural Networks Section Introduction
02:42
RBF Networks
15:39
RBF Approximations
08:39
What Happened to Infinite Dimensionality?
02:54
Build Your Own RBF Network
03:54
Relationship to Deep Learning Neural Networks
06:51
Neural Network-SVM Mashup
07:16
Neural Networks Section Conclusion
02:37
What is the Appendix?
03:47
Pre-Installation Check
04:13
Anaconda Environment Setup
20:21
How to install Numpy, Scipy, Matplotlib, Pandas, PyTorch, and TensorFlow
17:33
How to Code Yourself (part 1)
15:55
How to Code Yourself (part 2)
09:24
Proof that using Jupyter Notebook is the same as not using it
12:29
Python 2 vs Python 3
04:38
How to Succeed in this Course (Long Version)
10:25
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
22:05
What order should I take your courses in? (part 1)
11:19
What order should I take your courses in? (part 2)
16:07
Where to get discount coupons and FREE deep learning material
05:49
Introduction
02:21
Course Objectives
04:55
Course Outline
05:50
Where to get the code and data
05:49
Beginner's Corner: Section Introduction
05:19
Image Classification with SVMs
06:01
Spam Detection with SVMs
11:48
Medical Diagnosis with SVMs
05:16
Regression with SVMs
05:36
Cross-Validation
07:21
How do you get the data? How do you process the data?
05:22
Basic Geometry
10:52
Normal Vectors
03:42
Logistic Regression Review
09:46
Loss Function and Regularization
04:10
Prediction Confidence
07:26
Nonlinear Problems
09:59
Linear Classifiers Section Conclusion
04:26
Suggestion Box
03:10
Linear SVM Section Introduction and Outline
03:19
Linear SVM Problem Setup and Definitions
04:31
Margins
08:52
Linear SVM Objective
11:01
Linear and Quadratic Programming
12:32
Slack Variables
07:26
Hinge Loss (and its Relationship to Logistic Regression)
06:23
Linear SVM with Gradient Descent
03:11
Linear SVM with Gradient Descent (Code)
05:07
Linear SVM Section Summary
04:15
Duality Section Introduction
03:44
Duality and Lagrangians (part 1)
13:02
Lagrangian Duality (part 2)
07:09
Relationship to Linear Programming
04:20
Predictions and Support Vectors
09:17
Why Transform Primal to Dual?
03:27
Duality Section Conclusion
02:55
Kernel Methods Section Introduction
03:48
The Kernel Trick
08:12
Polynomial Kernel
06:07
Gaussian Kernel
05:14
Using the Gaussian Kernel
07:10
Why does the Gaussian Kernel correspond to infinite-dimensional features?
04:40
Other Kernels
07:05
Mercer's Condition
06:25
Kernel Methods Section Summary
02:42
Dual with Slack Variables
10:41
Simple Approaches to Implementation
06:26
SVM with Projected Gradient Descent Code
08:20
Kernel SVM Gradient Descent with Primal (Theory)
04:31
Kernel SVM Gradient Descent with Primal (Code)
04:56
SMO (Sequential Minimal Optimization)
09:33
Support Vector Regression
05:27
Multiclass Classification
04:35
Neural Networks Section Introduction
02:42
RBF Networks
15:39
RBF Approximations
08:39
What Happened to Infinite Dimensionality?
02:54
Build Your Own RBF Network
03:54
Relationship to Deep Learning Neural Networks
06:51
Neural Network-SVM Mashup
07:16
Neural Networks Section Conclusion
02:37
What is the Appendix?
03:47
Pre-Installation Check
04:13
Anaconda Environment Setup
20:21
How to install Numpy, Scipy, Matplotlib, Pandas, PyTorch, and TensorFlow
17:33
How to Code Yourself (part 1)
15:55
How to Code Yourself (part 2)
09:24
Proof that using Jupyter Notebook is the same as not using it
12:29
Python 2 vs Python 3
04:38
How to Succeed in this Course (Long Version)
10:25
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
22:05
What order should I take your courses in? (part 1)
11:19
What order should I take your courses in? (part 2)
16:07
Where to get discount coupons and FREE deep learning material
05:49

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