# Machine Learning and AI: 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

## Course content

- Preview02:20
- Preview04:54
- Preview05:49
- 05:48Where to get the code and data

## Requirements

- Calculus, Matrix Arithmetic / Geometry, Basic 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!

"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)

## 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

## Instructor

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.

I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.

Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.

I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.

My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School.

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.