Machine Learning and AI: Support Vector Machines in Python
Course Description
Artificial Intelligence and Data Science Algorithms in Python for Classification and Regression
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, stepbystep 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 CrossEntropy 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 infinitedimensional 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 endtoend 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
What you will 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