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

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03/09/19 at 3:13pm GMT+1

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Description

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/

File list

[FTUForum.com] [UDEMY] Machine Learning and AI Support Vector Machines in Python [FTU]
1. Welcome/1. Introduction.mp4 16.1 MB
1. Welcome/1. Introduction.vtt 2.7 KB
1. Welcome/2. Course Objectives.mp4 37.2 MB
1. Welcome/2. Course Objectives.vtt 5.7 KB
1. Welcome/3. Course Outline.mp4 31.3 MB
1. Welcome/3. Course Outline.vtt 6.7 KB
1. Welcome/4. Where to get the code and data.mp4 39 MB
1. Welcome/4. Where to get the code and data.vtt 7 KB
2. Beginner_s Corner/1. Beginner_s Corner Section Introduction.mp4 34 MB
2. Beginner_s Corner/1. Beginner_s Corner Section Introduction.vtt 6.2 KB
2. Beginner_s Corner/2. Image Classification with SVMs.mp4 36.5 MB
2. Beginner_s Corner/2. Image Classification with SVMs.vtt 6.4 KB
2. Beginner_s Corner/3. Spam Detection with SVMs.mp4 101.5 MB
2. Beginner_s Corner/3. Spam Detection with SVMs.vtt 12.4 KB
2. Beginner_s Corner/4. Medical Diagnosis with SVMs.mp4 47.9 MB
2. Beginner_s Corner/4. Medical Diagnosis with SVMs.vtt 6 KB
2. Beginner_s Corner/5. Regression with SVMs.mp4 50.9 MB
2. Beginner_s Corner/5. Regression with SVMs.vtt 5.6 KB
2. Beginner_s Corner/6. Cross-Validation.mp4 54.6 MB
2. Beginner_s Corner/6. Cross-Validation.vtt 8.3 KB
2. Beginner_s Corner/7. How do you get the data How do you process the data.mp4 28.8 MB
2. Beginner_s Corner/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
3. Review of Linear Classifiers/1. Basic Geometry.vtt 11.4 KB
3. Review of Linear Classifiers/2. Normal Vectors.mp4 14.8 MB
3. Review of Linear Classifiers/2. Normal Vectors.vtt 3.6 KB
3. Review of Linear Classifiers/3. Logistic Regression Review.mp4 39.9 MB
3. Review of Linear Classifiers/3. Logistic Regression Review.vtt 10.7 KB
3. Review of Linear Classifiers/4. Loss Function and Regularization.mp4 16.1 MB
3. Review of Linear Classifiers/4. Loss Function and Regularization.vtt 4.3 KB
3. Review of Linear Classifiers/5. Prediction Confidence.mp4 30.6 MB
3. Review of Linear Classifiers/5. Prediction Confidence.vtt 7.9 KB
3. Review of Linear Classifiers/6. Nonlinear Problems.mp4 47 MB
3. Review of Linear Classifiers/6. Nonlinear Problems.vtt 10.4 KB
3. Review of Linear Classifiers/7. Linear Classifiers Section Conclusion.mp4 19.3 MB
3. Review of Linear Classifiers/7. Linear Classifiers Section Conclusion.vtt 4.7 KB
4. Linear SVM/1. Linear SVM Section Introduction and Outline.mp4 17.7 MB
4. Linear SVM/1. Linear SVM Section Introduction and Outline.vtt 3.7 KB
4. Linear SVM/10. Linear SVM Section Summary.mp4 19 MB
4. Linear SVM/10. Linear SVM Section Summary.vtt 4.9 KB
4. Linear SVM/2. Linear SVM Problem Setup and Definitions.mp4 22.8 MB
4. Linear SVM/2. Linear SVM Problem Setup and Definitions.vtt 5.1 KB
4. Linear SVM/3. Margins.mp4 41.5 MB
4. Linear SVM/3. Margins.vtt 8.6 KB
4. Linear SVM/4. Linear SVM Objective.mp4 49.2 MB
4. Linear SVM/4. Linear SVM Objective.vtt 11.6 KB
4. Linear SVM/5. Linear and Quadratic Programming.mp4 64.2 MB
4. Linear SVM/5. Linear and Quadratic Programming.vtt 13.2 KB
4. Linear SVM/6. Slack Variables.mp4 38.7 MB
4. Linear SVM/6. Slack Variables.vtt 7.9 KB
4. Linear SVM/7. Hinge Loss (and its Relationship to Logistic Regression).mp4 29.7 MB
4. Linear SVM/7. Hinge Loss (and its Relationship to Logistic Regression).vtt 6.7 KB
4. Linear SVM/8. Linear SVM with Gradient Descent.mp4 15.7 MB
4. Linear SVM/8. Linear SVM with Gradient Descent.vtt 3.1 KB
4. Linear SVM/9. Linear SVM with Gradient Descent (Code).mp4 51.9 MB
4. Linear SVM/9. Linear SVM with Gradient Descent (Code).vtt 5.3 KB
5. Duality/1. Duality Section Introduction.mp4 14.7 MB
5. Duality/1. Duality Section Introduction.vtt 4.2 KB
5. Duality/2. Duality and Lagrangians (part 1).mp4 58.7 MB
5. Duality/2. Duality and Lagrangians (part 1).vtt 13.6 KB
5. Duality/3. Lagrangian Duality (part 2).mp4 29.2 MB
5. Duality/3. Lagrangian Duality (part 2).vtt 6.7 KB
5. Duality/4. Relationship to Linear Programming.mp4 20.1 MB
5. Duality/4. Relationship to Linear Programming.vtt 4.6 KB
5. Duality/5. Predictions and Support Vectors.mp4 38.9 MB
5. Duality/5. Predictions and Support Vectors.vtt 9.6 KB
5. Duality/6. Why Transform Primal to Dual.mp4 16.9 MB
5. Duality/6. Why Transform Primal to Dual.vtt 3.8 KB
5. Duality/7. Duality Section Conclusion.mp4 13.2 MB
5. Duality/7. Duality Section Conclusion.vtt 3 KB
6. Kernel Methods/1. Kernel Methods Section Introduction.mp4 19.1 MB
6. Kernel Methods/1. Kernel Methods Section Introduction.vtt 3.9 KB
6. Kernel Methods/2. The Kernel Trick.mp4 37.2 MB
6. Kernel Methods/2. The Kernel Trick.vtt 8 KB
6. Kernel Methods/3. Polynomial Kernel.mp4 25.4 MB
6. Kernel Methods/3. Polynomial Kernel.vtt 5.9 KB
6. Kernel Methods/4. Gaussian Kernel.mp4 27 MB
6. Kernel Methods/4. Gaussian Kernel.vtt 5.3 KB
6. Kernel Methods/5. Using the Gaussian Kernel.mp4 36 MB
6. Kernel Methods/5. Using the Gaussian Kernel.vtt 7.6 KB
6. Kernel Methods/6. Why does the Gaussian Kernel correspond to infinite-dimensional features.mp4 19.8 MB
6. Kernel Methods/6. Why does the Gaussian Kernel correspond to infinite-dimensional features.vtt 4.4 KB
6. Kernel Methods/7. Other Kernels.mp4 32.4 MB
6. Kernel Methods/7. Other Kernels.vtt 7.2 KB
6. Kernel Methods/8. Mercer_s Condition.mp4 27.6 MB
6. Kernel Methods/8. Mercer_s Condition.vtt 6.6 KB
6. Kernel Methods/9. Kernel Methods Section Summary.mp4 11.1 MB
6. Kernel Methods/9. Kernel Methods Section Summary.vtt 2.8 KB
7. Implementations and Extensions/1. Dual with Slack Variables.mp4 38.9 MB
7. Implementations and Extensions/1. Dual with Slack Variables.vtt 11.2 KB
7. Implementations and Extensions/2. Simple Approaches to Implementation.mp4 24.7 MB
7. Implementations and Extensions/2. Simple Approaches to Implementation.vtt 6.9 KB
7. Implementations and Extensions/3. SVM with Projected Gradient Descent Code.mp4 83.6 MB
7. Implementations and Extensions/3. SVM with Projected Gradient Descent Code.vtt 7.8 KB
7. Implementations and Extensions/4. Kernel SVM Gradient Descent with Primal (Theory).mp4 21.3 MB
7. Implementations and Extensions/4. Kernel SVM Gradient Descent with Primal (Theory).vtt 4.9 KB
7. Implementations and Extensions/5. Kernel SVM Gradient Descent with Primal (Code).mp4 58.7 MB
7. Implementations and Extensions/5. Kernel SVM Gradient Descent with Primal (Code).vtt 4.1 KB
7. Implementations and Extensions/6. SMO (Sequential Minimal Optimization).mp4 41.4 MB

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