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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

  • 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.

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

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)

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Rating 4.2 based on 8 ratings
Length 9 total hours
Starts On Demand (Start anytime)
Cost $16
From Udemy
Instructor Lazy Programmer Inc.
Download Videos Only via the Udemy mobile app
Language English
Subjects Data Science Business
Tags Data Science Business Development Data & Analytics

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What people are saying

machine learning

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

advanced linear algebra

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

arrive at certain

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.

deep dive into

First off, differently from Lazy's other courses, there are a couple of sections devoted to use the SVM API without having to deep dive into the theory with lots of practical advice (this is a really welcome idea for future courses).

develop an intuition

instructor knows his

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Rating 4.2 based on 8 ratings
Length 9 total hours
Starts On Demand (Start anytime)
Cost $16
From Udemy
Instructor Lazy Programmer Inc.
Download Videos Only via the Udemy mobile app
Language English
Subjects Data Science Business
Tags Data Science Business Development Data & Analytics

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