# Machine Learning and AI

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

• 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

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)

OpenCourser is an affiliate partner of Udemy and may earn a commission when you buy through our links.

Rating 4.4★ based on 15 ratings 9 total hours On Demand (Start anytime) \$0 Udemy Lazy Programmer Inc., Lazy Programmer Team Only via the Udemy mobile app English Data Science Business Data Science Business Development Data & Analytics

## What people are saying

machine learning

good course, even if very challenging; detailed and precise explanations good The_introductory_material_gave_me_the_impression_that_the_topic_is_outdated I've attended quite a few courses in Machine Learning from different teachers (including Lazy Programmer) and this is by far the best one.

muito didático e bem

Muito didático e bem completo.

complex ideas introduced progressively

The structure of the course is also solid, with more complex ideas introduced progressively through exercises and not all up front.

concepts intuitively after completing

I really felt I grasped the concepts intuitively after completing this course :) It covers the SVM from every angle and starts at the beginning with how to start using SVMs for different projects.

graphical presentation would help

Little of intuition and graphical presentation would help the learning process.

research scientist was astonishingly

I myself as a machine learning research scientist was astonishingly impressed by clarity of logistics from Lazy Programmer.

showing consistently without gaps

The concepts are taught in a fast and concise way and the instructor knows his field, showing consistently without gaps how to arrive at certain steps, which helps greatly to develop an intuition for the presented material, even for the novice.

arrive at certain steps

Though, be warned that you need a solid background in Probabilities and Advanced Linear Algebra (and patience) to get a grasp of SVM.

knows his field

make engaging presentations

Finally, Lazy uses storytelling to make engaging presentations.

own regression modeling

I used this course for my own regression modeling.

## Careers

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Technical Support, Helpdesk, Application Support \$59k

Vector/Cutco Sales Representative \$62k

IT / IS Support \$67k

Sales Support and Customer Support \$69k

IT Support/ Network Support Analyst \$71k

IS/IT Support \$76k

Application Support Analyst/Litigation Support \$79k

Supervisor Technical Support/Training support \$84k

Concept Artist/Vector Artist /Flash Animator \$86k

Assistant Vector Controller/Supv. \$87k

IT Support Analyst | PC Support \$88k

Vector Controller/Supv. \$119k

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