Machine Learning and AI: Support Vector Machines in Python – Udemy

(10 customer reviews)

$30

Category:

Description

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

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

Course content

  • Welcome
  • Beginner’s Corner
  • Review of Linear Classifiers
  • Linear SVM
  • Duality
  • Kernel Methods
  • Implementations and Extensions
  • Neural Networks (Beginner’s Corner 2)
  • Setting Up Your Environment (FAQ by Student Request)
  • Extra Help With Python Coding for Beginners (FAQ by Student Request)

10 reviews for Machine Learning and AI: Support Vector Machines in Python – Udemy

  1. Donny Phan

    Super practical. Lessons are catered towards anyone looking to find work in this industry. It felt very comprehensive and gave me a broad understanding of the programming spectrum

  2. Madhav raj Verma

    Thanks for your great effort. i am fully satisfied with this course the way you teach and your explanation are very clear ,The content you provide in your course no one can do this at this price.

  3. Sachin Gupta

    I really didn’t want to leave a low rating as Angela is a great teacher. The 1st half of this course was terrific. The 2nd half was terrible. Under the justification of “teaching students how to figure things out on their own”, pretty much all videos and all explanations were dropped. You were just told what to do, given links to documentation and told to figure it out on your own. I understand doing that to some degree, but to revert to that entirely for nearly half the content barely makes this a course. It’s just a list of things for you to learn, then you’re left on your own to learn them. The 2nd half was so bad, especially the data science component, that I didn’t bother finishing the course.

  4. Vincent Beaudet

    Amazing 40 days course.
    Angela is a great teacher.
    The other 60 days are all about web developement, interacting with web pages, on your own with little to no explanations. I did not expect that at all. I wanted to learn more about software and scripting.
    This left me disappointed , confused and i started to doubt myself. Not a fun experience after the amount of effort i’v put in this course.

    Exercices format and explanations for the first 40 days were worth it tho.

  5. Ben K

    Not just an introduction to python, but really helps you learn fundamental aspects of python and coding in general. Some parts may require some knowledge on the subject (data science comes to mind) and there is quite some web development in the course. So, a few areas were not completely to my liking (I would have liked to see it done differently), but this course deserves the 5 stars in my opinion.

  6. Omid Alikhel

    I found the method a bit difficult when a code is written and then changed back to something different, with no enough explanation of how something happened and where it came from or a step by step explanation of why something is happening, i have no doubt in the instructors talent, but we are beginners!

  7. Devang Jain

    The course is not updated and most of the solution codes don’t work and there are no video solutions towards the end

  8. Szymon Kozak

    I think that the course tutor is really good in giving right information to learn at the right time. Thanks to this fact, my understanding of coding in python after 29 days of learning is above my expectations.

  9. Begoña Ruiz Diaz

    Ha sido la mejor elección que podría haber hecho.

  10. Vaibhav Sachdeva

    I want to thank Angela for making such an amazing course. It really helped me explore more things with python.

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