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

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English

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3.05 GB

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1dca37e8db24f33437b3e2e63a250099ac69b11c

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Prom3th3uS :_verified_uploader:

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09-03-2019 14:06 (edited 09-03-2019 14:06) by Prom3th3uS

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File	Size
1. Welcome/1. Introduction.mp4	16.15 MB
1. Welcome/1. Introduction.vtt	2.69 kB
1. Welcome/2. Course Objectives.mp4	37.24 MB
1. Welcome/2. Course Objectives.vtt	5.72 kB
1. Welcome/3. Course Outline.mp4	31.30 MB
1. Welcome/3. Course Outline.vtt	6.68 kB
1. Welcome/4. Where to get the code and data.mp4	39.03 MB
1. Welcome/4. Where to get the code and data.vtt	6.98 kB
2. Beginner_s Corner/1. Beginner_s Corner Section Introduction.mp4	34.01 MB
2. Beginner_s Corner/1. Beginner_s Corner Section Introduction.vtt	6.23 kB
2. Beginner_s Corner/2. Image Classification with SVMs.mp4	36.49 MB
2. Beginner_s Corner/2. Image Classification with SVMs.vtt	6.37 kB
2. Beginner_s Corner/3. Spam Detection with SVMs.mp4	101.47 MB
2. Beginner_s Corner/3. Spam Detection with SVMs.vtt	12.42 kB
2. Beginner_s Corner/4. Medical Diagnosis with SVMs.mp4	47.91 MB
2. Beginner_s Corner/4. Medical Diagnosis with SVMs.vtt	6.05 kB
2. Beginner_s Corner/5. Regression with SVMs.mp4	50.90 MB
2. Beginner_s Corner/5. Regression with SVMs.vtt	5.63 kB
2. Beginner_s Corner/6. Cross-Validation.mp4	54.63 MB
2. Beginner_s Corner/6. Cross-Validation.vtt	8.33 kB
2. Beginner_s Corner/7. How do you get the data How do you process the data.mp4	28.83 MB
2. Beginner_s Corner/7. How do you get the data How do you process the data.vtt	6.68 kB
3. Review of Linear Classifiers/1. Basic Geometry.mp4	46.61 MB
3. Review of Linear Classifiers/1. Basic Geometry.vtt	11.41 kB
3. Review of Linear Classifiers/2. Normal Vectors.mp4	14.80 MB
3. Review of Linear Classifiers/2. Normal Vectors.vtt	3.64 kB
3. Review of Linear Classifiers/3. Logistic Regression Review.mp4	39.90 MB
3. Review of Linear Classifiers/3. Logistic Regression Review.vtt	10.69 kB
3. Review of Linear Classifiers/4. Loss Function and Regularization.mp4	16.15 MB
3. Review of Linear Classifiers/4. Loss Function and Regularization.vtt	4.30 kB
3. Review of Linear Classifiers/5. Prediction Confidence.mp4	30.65 MB
3. Review of Linear Classifiers/5. Prediction Confidence.vtt	7.92 kB
3. Review of Linear Classifiers/6. Nonlinear Problems.mp4	47.05 MB
3. Review of Linear Classifiers/6. Nonlinear Problems.vtt	10.41 kB
3. Review of Linear Classifiers/7. Linear Classifiers Section Conclusion.mp4	19.29 MB
3. Review of Linear Classifiers/7. Linear Classifiers Section Conclusion.vtt	4.69 kB
4. Linear SVM/1. Linear SVM Section Introduction and Outline.mp4	17.68 MB
4. Linear SVM/1. Linear SVM Section Introduction and Outline.vtt	3.74 kB
4. Linear SVM/10. Linear SVM Section Summary.mp4	18.99 MB
4. Linear SVM/10. Linear SVM Section Summary.vtt	4.88 kB
4. Linear SVM/2. Linear SVM Problem Setup and Definitions.mp4	22.84 MB
4. Linear SVM/2. Linear SVM Problem Setup and Definitions.vtt	5.11 kB
4. Linear SVM/3. Margins.mp4	41.49 MB
4. Linear SVM/3. Margins.vtt	8.56 kB
4. Linear SVM/4. Linear SVM Objective.mp4	49.17 MB
4. Linear SVM/4. Linear SVM Objective.vtt	11.64 kB
4. Linear SVM/5. Linear and Quadratic Programming.mp4	64.22 MB
4. Linear SVM/5. Linear and Quadratic Programming.vtt	13.19 kB
4. Linear SVM/6. Slack Variables.mp4	38.68 MB
4. Linear SVM/6. Slack Variables.vtt	7.95 kB
4. Linear SVM/7. Hinge Loss (and its Relationship to Logistic Regression).mp4	29.69 MB
4. Linear SVM/7. Hinge Loss (and its Relationship to Logistic Regression).vtt	6.68 kB
4. Linear SVM/8. Linear SVM with Gradient Descent.mp4	15.68 MB
4. Linear SVM/8. Linear SVM with Gradient Descent.vtt	3.13 kB
4. Linear SVM/9. Linear SVM with Gradient Descent (Code).mp4	51.93 MB
4. Linear SVM/9. Linear SVM with Gradient Descent (Code).vtt	5.31 kB
5. Duality/1. Duality Section Introduction.mp4	14.72 MB
5. Duality/1. Duality Section Introduction.vtt	4.22 kB
5. Duality/2. Duality and Lagrangians (part 1).mp4	58.69 MB
5. Duality/2. Duality and Lagrangians (part 1).vtt	13.63 kB
5. Duality/3. Lagrangian Duality (part 2).mp4	29.19 MB
5. Duality/3. Lagrangian Duality (part 2).vtt	6.74 kB
5. Duality/4. Relationship to Linear Programming.mp4	20.12 MB
5. Duality/4. Relationship to Linear Programming.vtt	4.55 kB
5. Duality/5. Predictions and Support Vectors.mp4	38.88 MB
5. Duality/5. Predictions and Support Vectors.vtt	9.57 kB
5. Duality/6. Why Transform Primal to Dual.mp4	16.93 MB
5. Duality/6. Why Transform Primal to Dual.vtt	3.75 kB
5. Duality/7. Duality Section Conclusion.mp4	13.22 MB
5. Duality/7. Duality Section Conclusion.vtt	2.99 kB
6. Kernel Methods/1. Kernel Methods Section Introduction.mp4	19.13 MB
6. Kernel Methods/1. Kernel Methods Section Introduction.vtt	3.87 kB
6. Kernel Methods/2. The Kernel Trick.mp4	37.25 MB
6. Kernel Methods/2. The Kernel Trick.vtt	8.03 kB
6. Kernel Methods/3. Polynomial Kernel.mp4	25.37 MB
6. Kernel Methods/3. Polynomial Kernel.vtt	5.91 kB
6. Kernel Methods/4. Gaussian Kernel.mp4	26.96 MB
6. Kernel Methods/4. Gaussian Kernel.vtt	5.26 kB
6. Kernel Methods/5. Using the Gaussian Kernel.mp4	36.01 MB
6. Kernel Methods/5. Using the Gaussian Kernel.vtt	7.64 kB
6. Kernel Methods/6. Why does the Gaussian Kernel correspond to infinite-dimensional features.mp4	19.85 MB
6. Kernel Methods/6. Why does the Gaussian Kernel correspond to infinite-dimensional features.vtt	4.40 kB
6. Kernel Methods/7. Other Kernels.mp4	32.44 MB
6. Kernel Methods/7. Other Kernels.vtt	7.23 kB
6. Kernel Methods/8. Mercer_s Condition.mp4	27.57 MB
6. Kernel Methods/8. Mercer_s Condition.vtt	6.57 kB
6. Kernel Methods/9. Kernel Methods Section Summary.mp4	11.14 MB
6. Kernel Methods/9. Kernel Methods Section Summary.vtt	2.82 kB
7. Implementations and Extensions/1. Dual with Slack Variables.mp4	38.93 MB
7. Implementations and Extensions/1. Dual with Slack Variables.vtt	11.20 kB
7. Implementations and Extensions/2. Simple Approaches to Implementation.mp4	24.65 MB
7. Implementations and Extensions/2. Simple Approaches to Implementation.vtt	6.93 kB
7. Implementations and Extensions/3. SVM with Projected Gradient Descent Code.mp4	83.60 MB
7. Implementations and Extensions/3. SVM with Projected Gradient Descent Code.vtt	7.80 kB
7. Implementations and Extensions/4. Kernel SVM Gradient Descent with Primal (Theory).mp4	21.35 MB
7. Implementations and Extensions/4. Kernel SVM Gradient Descent with Primal (Theory).vtt	4.89 kB
7. Implementations and Extensions/5. Kernel SVM Gradient Descent with Primal (Code).mp4	58.72 MB
7. Implementations and Extensions/5. Kernel SVM Gradient Descent with Primal (Code).vtt	4.09 kB
7. Implementations and Extensions/6. SMO (Sequential Minimal Optimization).mp4	41.42 MB
7. Implementations and Extensions/6. SMO (Sequential Minimal Optimization).vtt	10.54 kB
7. Implementations and Extensions/7. Support Vector Regression.mp4	27.24 MB
7. Implementations and Extensions/7. Support Vector Regression.vtt	5.82 kB
7. Implementations and Extensions/8. Multiclass Classification.mp4	19.08 MB
7. Implementations and Extensions/8. Multiclass Classification.vtt	4.89 kB
8. Neural Networks (Beginner_s Corner 2)/1. Neural Networks Section Introduction.mp4	15.61 MB
8. Neural Networks (Beginner_s Corner 2)/1. Neural Networks Section Introduction.vtt	3.07 kB
8. Neural Networks (Beginner_s Corner 2)/2. RBF Networks.mp4	79.54 MB
8. Neural Networks (Beginner_s Corner 2)/2. RBF Networks.vtt	17.03 kB
8. Neural Networks (Beginner_s Corner 2)/3. RBF Approximations.mp4	44.41 MB
8. Neural Networks (Beginner_s Corner 2)/3. RBF Approximations.vtt	9.37 kB
8. Neural Networks (Beginner_s Corner 2)/4. What Happened to Infinite Dimensionality.mp4	12.57 MB
8. Neural Networks (Beginner_s Corner 2)/4. What Happened to Infinite Dimensionality.vtt	2.90 kB
8. Neural Networks (Beginner_s Corner 2)/5. Build Your Own RBF Network.mp4	39.11 MB
8. Neural Networks (Beginner_s Corner 2)/5. Build Your Own RBF Network.vtt	3.98 kB
8. Neural Networks (Beginner_s Corner 2)/6. Relationship to Deep Learning Neural Networks.mp4	33.75 MB
8. Neural Networks (Beginner_s Corner 2)/6. Relationship to Deep Learning Neural Networks.vtt	7.78 kB
8. Neural Networks (Beginner_s Corner 2)/7. Neural Network-SVM Mashup.mp4	72.29 MB
8. Neural Networks (Beginner_s Corner 2)/7. Neural Network-SVM Mashup.vtt	7.27 kB
8. Neural Networks (Beginner_s Corner 2)/8. Neural Networks Section Conclusion.mp4	11.83 MB
8. Neural Networks (Beginner_s Corner 2)/8. Neural Networks Section Conclusion.vtt	2.83 kB
9. Appendix/1. What is the Appendix.mp4	25.44 MB
9. Appendix/1. What is the Appendix.vtt	3.29 kB
9. Appendix/10. What order should I take your courses in (part 1).mp4	88.41 MB
9. Appendix/10. What order should I take your courses in (part 1).vtt	14.17 kB
9. Appendix/11. What order should I take your courses in (part 2).mp4	123.00 MB
9. Appendix/11. What order should I take your courses in (part 2).vtt	20.24 kB
9. Appendix/12. [Bonus] Where to get discount coupons and FREE deep learning material.mp4	22.49 MB
9. Appendix/12. [Bonus] Where to get discount coupons and FREE deep learning material.vtt	2.91 kB
9. Appendix/2. Windows-Focused Environment Setup 2018.mp4	194.35 MB
9. Appendix/2. Windows-Focused Environment Setup 2018.vtt	17.34 kB
9. Appendix/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4	167.01 MB
9. Appendix/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt	12.59 kB
9. Appendix/4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4	117.69 MB
9. Appendix/4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt	27.68 kB
9. Appendix/5. How to Succeed in this Course (Long Version).mp4	39.25 MB
9. Appendix/5. How to Succeed in this Course (Long Version).vtt	12.83 kB
9. Appendix/6. How to Code by Yourself (part 1).mp4	82.57 MB
9. Appendix/6. How to Code by Yourself (part 1).vtt	19.38 kB
9. Appendix/7. How to Code by Yourself (part 2).mp4	56.69 MB
9. Appendix/7. How to Code by Yourself (part 2).vtt	11.44 kB
9. Appendix/8. Proof that using Jupyter Notebook is the same as not using it.mp4	78.29 MB
9. Appendix/8. Proof that using Jupyter Notebook is the same as not using it.vtt	12.31 kB
9. Appendix/9. Python 2 vs Python 3.mp4	30.25 MB
9. Appendix/9. Python 2 vs Python 3.vtt	5.35 kB
Discuss.FTUForum.com.html	31.89 kB
FreeCoursesOnline.Me.html	108.30 kB
FTUForum.com.html	100.44 kB
How you can help Team-FTU.txt	235.00 B
Torrent Downloaded From GloDls.to.txt	84.00 B
[TGx]Downloaded from torrentgalaxy.org.txt	524.00 B

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/