[UDEMY] Machine Learning and AI Support Vector Machines in Python [FTU]

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  • Last checkedMar. 11th '19
  • Date uploadedMar. 10th '19
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Artificial Intelligence and Data Science Algorithms in Python for Classification and Regression

Created by : Lazy Programmer Inc.
Last updated : 1/2019
Language : English
Caption (CC) : Included
Torrent Contains : 150 Files, 9 Folders
Course Source : https://www.udemy.com/support-vector-machines-in-python/

What you'll learn

• Apply SVMs to practical applications: image recognition, spam detection, medical diagnosis, and regression analysis
• Understand the theory behind SVMs from scratch (basic geometry)
• Use Lagrangian Duality to derive the Kernel SVM
• Understand how Quadratic Programming is applied to SVM
• Support Vector Regression
• Polynomial Kernel, Gaussian Kernel, and Sigmoid Kernel
• Build your own RBF Network and other Neural Networks based on SVM

Requirements

• Calculus, Linear Algebra, Probability
• Python and Numpy coding
• Logistic Regression

Description

Support Vector Machines (SVM) are one of the most powerful machine learning models around, and this topic has been one that students have requested ever since I started making courses.

These days, everyone seems to be talking about deep learning, but in fact there was a time when support vector machines were seen as superior to neural networks. One of the things you’ll learn about in this course is that a support vector machine actually is a neural network, and they essentially look identical if you were to draw a diagram.

The toughest obstacle to overcome when you’re learning about support vector machines is that they are very theoretical. This theory very easily scares a lot of people away, and it might feel like learning about support vector machines is beyond your ability. Not so!

In this course, we take a very methodical, step-by-step approach to build up all the theory you need to understand how the SVM really works. We are going to use Logistic Regression as our starting point, which is one of the very first things you learn about as a student of machine learning. So if you want to understand this course, just have a good intuition about Logistic Regression, and by extension have a good understanding of the geometry of lines, planes, and hyperplanes.

This course will cover the critical theory behind SVMs:

• Linear SVM derivation
• Hinge loss (and its relation to the Cross-Entropy loss)
• Quadratic programming (and Linear programming review)
• Slack variables
• Lagrangian Duality
• Kernel SVM (nonlinear SVM)
• Polynomial Kernels, Gaussian Kernels, Sigmoid Kernels, and String Kernels
• Learn how to achieve an infinite-dimensional feature expansion
• Projected Gradient Descent
• SMO (Sequential Minimal Optimization)
• RBF Networks (Radial Basis Function Neural Networks)
• Support Vector Regression (SVR)
• Multiclass Classification

For those of you who are thinking, "theory is not for me", there’s lots of material in this course for you too!

In this course, there will be not just one, but two full sections devoted to just the practical aspects of how to make effective use of the SVM.

We’ll do end-to-end examples of real, practical machine learning applications, such as:

• Image recognition
• Spam detection
• Medical diagnosis
• Regression analysis

For more advanced students, there are also plenty of coding exercises where you will get to try different approaches to implementing SVMs.
These are implementations that you won't find anywhere else in any other course.
Thanks for reading, and I’ll see you in class!

HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

• Calculus
• Linear Algebra / Geometry
• Basic Probability
• Logistic Regression
• Python coding: if/else, loops, lists, dicts, sets
• Numpy coding: matrix and vector operations, loading a CSV file

TIPS (for getting through the course):

• Watch it at 2x.
• Take handwritten notes. This will drastically increase your ability to retain the information.
• Write down the equations. If you don't, I guarantee it will just look like gibberish.
• Ask lots of questions on the discussion board. The more the better!
• The best exercises will take you days or weeks to complete.
• Write code yourself, don't just sit there and look at my code. This is not a philosophy course!

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

• Check out the lecture "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)

Who this course is for :

• Beginners who want to know how to use the SVM for practical problems
• Experts who want to know all the theory behind the SVM
• Professionals who want to know how to effectively tune the SVM for their application.

For More Udemy Free Courses >>> http://www.freetutorials.eu
For more Lynda and other Courses >>> https://www.freecoursesonline.me/
Our Forum for discussion >>> https://discuss.freetutorials.eu/



Files:

[FTUForum.com] [UDEMY] Machine Learning and AI Support Vector Machines in Python [FTU] 1. Welcome
  • 1. Introduction.mp4 (16.1 MB)
  • 1. Introduction.vtt (2.7 KB)
  • 2. Course Objectives.mp4 (37.2 MB)
  • 2. Course Objectives.vtt (5.7 KB)
  • 3. Course Outline.mp4 (31.3 MB)
  • 3. Course Outline.vtt (6.7 KB)
  • 4. Where to get the code and data.mp4 (39.0 MB)
  • 4. Where to get the code and data.vtt (7.0 KB)
2. Beginner_s Corner
  • 1. Beginner_s Corner Section Introduction.mp4 (34.0 MB)
  • 1. Beginner_s Corner Section Introduction.vtt (6.2 KB)
  • 2. Image Classification with SVMs.mp4 (36.5 MB)
  • 2. Image Classification with SVMs.vtt (6.4 KB)
  • 3. Spam Detection with SVMs.mp4 (101.5 MB)
  • 3. Spam Detection with SVMs.vtt (12.4 KB)
  • 4. Medical Diagnosis with SVMs.mp4 (47.9 MB)
  • 4. Medical Diagnosis with SVMs.vtt (6.0 KB)
  • 5. Regression with SVMs.mp4 (50.9 MB)
  • 5. Regression with SVMs.vtt (5.6 KB)
  • 6. Cross-Validation.mp4 (54.6 MB)
  • 6. Cross-Validation.vtt (8.3 KB)
  • 7. How do you get the data How do you process the data.mp4 (28.8 MB)
  • 7. How do you get the data How do you process the data.vtt (6.7 KB)
3. Review of Linear Classifiers
  • 1. Basic Geometry.mp4 (46.6 MB)
  • 1. Basic Geometry.vtt (11.4 KB)
  • 2. Normal Vectors.mp4 (14.8 MB)
  • 2. Normal Vectors.vtt (3.6 KB)
  • 3. Logistic Regression Review.mp4 (39.9 MB)
  • 3. Logistic Regression Review.vtt (10.7 KB)
  • 4. Loss Function and Regularization.mp4 (16.1 MB)
  • 4. Loss Function and Regularization.vtt (4.3 KB)
  • 5. Prediction Confidence.mp4 (30.6 MB)
  • 5. Prediction Confidence.vtt (7.9 KB)
  • 6. Nonlinear Problems.mp4 (47.0 MB)
  • 6. Nonlinear Problems.vtt (10.4 KB)
  • 7. Linear Classifiers Section Conclusion.mp4 (19.3 MB)
  • 7. Linear Classifiers Section Conclusion.vtt (4.7 KB)
4. Linear SVM
  • 10. Linear SVM Section Summary.mp4 (19.0 MB)
  • 10. Linear SVM Section Summary.vtt (4.9 KB)
  • 1. Linear SVM Section Introduction and Outline.mp4 (17.7 MB)
  • 1. Linear SVM Section Introduction and Outline.vtt (3.7 KB)
  • 2. Linear SVM Problem Setup and Definitions.mp4 (22.8 MB)
  • 2. Linear SVM Problem Setup and Definitions.vtt (5.1 KB)
  • 3. Margins.mp4 (41.5 MB)
  • 3. Margins.vtt (8.6 KB)
  • 4. Linear SVM Objective.mp4 (49.2 MB)
  • 4. Linear SVM Objective.vtt (11.6 KB)
  • 5. Linear and Quadratic Programming.mp4 (64.2 MB)
  • 5. Linear and Quadratic Programming.vtt (13.2 KB)
  • 6. Slack Variables.mp4 (38.7 MB)
  • 6. Slack Variables.vtt (7.9 KB)
  • 7. Hinge Loss (and its Relationship to Logistic Regression).mp4 (29.7 MB)
  • 7. Hinge Loss (and its Relationship to Logistic Regression).vtt (6.7 KB)
  • 8. Linear SVM with Gradient Descent.mp4 (15.7 MB)
  • 8. Linear SVM with Gradient Descent.vtt (3.1 KB)
  • 9. Linear SVM with Gradient Descent (Code).mp4 (51.9 MB)
  • 9. Linear SVM with Gradient Descent (Code).vtt (5.3 KB)
5. Duality
  • 1. Duality Section Introduction.mp4 (14.7 MB)
  • 1. Duality Section Introduction.vtt (4.2 KB)
  • 2. Duality and Lagrangians (part 1).mp4 (58.7 MB)
  • 2. Duality and Lagrangians (part 1).vtt (13.6 KB)
  • 3. Lagrangian Duality (part 2).mp4 (29.2 MB)
  • 3. Lagrangian Duality (part 2).vtt (6.7 KB)
  • 4. Relationship to Linear Programming.mp4 (20.1 MB)
  • 4. Relationship to Linear Programming.vtt (4.6 KB)
  • 5. Predictions and Support Vectors.mp4 (38.9 MB)
  • 5. Predictions and Support Vectors.vtt (9.6 KB)
  • 6. Why Transform Primal to Dual.mp4 (16.9 MB)
  • 6. Why Transform Primal to Dual.vtt (3.8 KB)
  • 7. Duality Section Conclusion.mp4 (13.2 MB)
  • 7. Duality Section Conclusion.vtt (3.0 KB)
6. Kernel Methods
  • 1. Kernel Methods Section Introduction.mp4 (19.1 MB)
  • 1. Kernel Methods Section Introduction.vtt (3.9 KB)
  • 2. The Kernel Trick.mp4 (37.2 MB)
  • 2. The Kernel Trick.vtt (8.0 KB)
  • 3. Polynomial Kernel.mp4 (25.4 MB)
  • 3. Polynomial Kernel.vtt (5.9 KB)
  • 4. Gaussian Kernel.mp4 (27.0 MB)
  • 4. Gaussian Kernel.vtt (5.3 KB)
  • 5. Using the Gaussian Kernel.mp4 (36.0 MB)
  • 5. Using the Gaussian Kernel.vtt (7.6 KB)
  • 6. Why does the Gaussian Kernel correspond to infinite-dimensional features.mp4 (19.8 MB)
  • 6. Why does the Gaussian Kernel correspond to infinite-dimensional features.vtt (4.4 KB)
  • 7. Other Kernels.mp4 (32.4 MB)
  • 7. Other Kernels.vtt (7.2 KB)
  • 8. Mercer_s Condition.mp4 (27.6 MB)
  • 8. Mercer_s Condition.vtt (6.6 KB)
  • 9. Kernel Methods Section Summary.mp4 (11.1 MB)
  • 9. Kernel Methods Section Summary.vtt (2.8 KB)
7. Implementations and Extensions
  • 1. Dual with Slack Variables.mp4 (38.9 MB)
  • 1. Dual with Slack Variables.vtt (11.2 KB)
  • 2. Simple Approaches to Implementation.mp4 (24.7 MB)
  • 2. Simple Approaches to Implementation.vtt (6.9 KB)
  • 3. SVM with Projected Gradient Descent Code.mp4 (83.6 MB)
  • 3. SVM with Projected Gradient Descent Code.vtt (7.8 KB)
  • 4. Kernel SVM Gradient Descent with Primal (Theory).mp4 (21.3 MB)
  • 4. Kernel SVM Gradient Descent with Primal (Theory).vtt (4.9 KB)
  • 5. Kernel SVM Gradient Descent with Primal (Code).mp4 (58.7 MB)
  • 5. Kernel SVM Gradient Descent with Primal (Code).vtt (4.1 KB)
  • 6. SMO (Sequential Minimal Optimization).mp4 (41.4 MB)
  • 6. SMO (Sequential Minimal Optimization).vtt (10.5 KB)
  • 7. Support Vector Regression.mp4 (27.2 MB)
  • 7. Support Vector Regression.vtt (5.8 KB)
  • 8. Multiclass Classification.mp4 (19.1 MB)
  • 8. Multiclass Classification.v

Code:

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