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Courses>Data Science>Machine Learning and AI: Support Vector Machines in Python

DevelopmentMachine Learning and AI: Support Vector Machines in Python

Price:Paid

Length:9 hours

Content type:video

level:expert

Updated:23 February 2024

Published:21 August 2022

Similar courses

Opportunities

Courses>Data Science>Machine Learning and AI: Support Vector Machines in Python

Machine Learning and AI: Support Vector Machines in PythonWhat you will learn

1. Apply SVMs to practical applications: image recognition, spam detection, medical diagnosis, and regression analysis

2. Understand the theory behind SVMs from scratch (basic geometry)

3. Use Lagrangian Duality to derive the Kernel SVM

4. Understand how Quadratic Programming is applied to SVM

5. Support Vector Regression

6. Polynomial Kernel, Gaussian Kernel, and Sigmoid Kernel

7. Build your own RBF Network and other Neural Networks based on SVM

Target audiences

1. Beginners who want to know how to use the SVM for practical problems

2. Experts who want to know all the theory behind the SVM

3. Professionals who want to know how to effectively tune the SVM for their application

Requirements

1. Calculus, Matrix Arithmetic / Geometry, Basic Probability

2. Python and Numpy coding

3. Logistic Regression

FAQ

1. How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

2. Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!

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!

"If you can't implement it, you don't understand it"

Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...

Suggested Prerequisites:

Calculus

Matrix Arithmetic / Geometry

Basic Probability

Logistic Regression

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations, loading a CSV file

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

Similar courses

Opportunities

Make the most out of your online education

Copyright © 2021 Careertail.

All rights reserved

All rights reserved

Quick Links

Get StartedLog InAbout UsCourses

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BlogContactsPrivacy PolicyCookie PolicyTerms and Conditions

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Courses>Data Science>Machine Learning and AI: Support Vector Machines in Python

DevelopmentMachine Learning and AI: Support Vector Machines in Python

Price:Paid

Length:9 hours

Content type:video

level:expert

Updated:23 February 2024

Published:21 August 2022

Similar courses

Opportunities

Courses>Data Science>Machine Learning and AI: Support Vector Machines in Python

Machine Learning and AI: Support Vector Machines in PythonWhat you will learn

1. Apply SVMs to practical applications: image recognition, spam detection, medical diagnosis, and regression analysis

2. Understand the theory behind SVMs from scratch (basic geometry)

3. Use Lagrangian Duality to derive the Kernel SVM

4. Understand how Quadratic Programming is applied to SVM

5. Support Vector Regression

6. Polynomial Kernel, Gaussian Kernel, and Sigmoid Kernel

7. Build your own RBF Network and other Neural Networks based on SVM

Target audiences

1. Beginners who want to know how to use the SVM for practical problems

2. Experts who want to know all the theory behind the SVM

3. Professionals who want to know how to effectively tune the SVM for their application

Requirements

1. Calculus, Matrix Arithmetic / Geometry, Basic Probability

2. Python and Numpy coding

3. Logistic Regression

FAQ

1. How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

2. Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!

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!

"If you can't implement it, you don't understand it"

Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...

Suggested Prerequisites:

Calculus

Matrix Arithmetic / Geometry

Basic Probability

Logistic Regression

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations, loading a CSV file

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

Similar courses

Opportunities

Make the most out of your online education

Copyright © 2021 Careertail.

All rights reserved

All rights reserved

Quick Links

Get StartedLog InAbout UsCourses

Company

BlogContactsPrivacy PolicyCookie PolicyTerms and Conditions

Stay up to date

Trustpilot