Machine Learning and AI: Support Vector Machines in Python

Artificial Intelligence and Data Science Algorithms in Python for Classification and Regression

4.68 (1580 reviews)
Udemy
platform
English
language
Data Science
category
Machine Learning and AI: Support Vector Machines in Python
26,889
students
9 hours
content
Apr 2024
last update
$69.99
regular price

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

Why take this course?

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)


UNIQUE FEATURES

  • Every line of code explained in detail - email me any time if you disagree

  • No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch

  • Not afraid of university-level math - get important details about algorithms that other courses leave out

Screenshots

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Our review

📂 **Course Overview** The course in question is a comprehensive guide to Support Vector Machines (SVMs), a crucial algorithm in the field of Machine Learning (ML). The global course rating stands at an impressive 4.68, indicating high satisfaction among learners. The reviews are largely positive, with many learners commending the course for its structured approach and clear explanations. **Pros:** - **Thorough Coverage:** The course is described as the "most complete course about SVMs out there," offering a detailed exploration of the subject matter. - **Quality Content:** Learners appreciate the "clear and intuitive understanding" of the mathematical concepts involved, which are vital for grasping SVM techniques. - **Engaging Lectures:** The instructor's teaching style is often highlighted as a strength, with several learners praising his ability to engage and inspire students to delve deeper into ML. - **Real-World Application:** The course is not just theoretical but also practical, providing insights into how SVMs can be applied in the real world. - **Expertise:** The instructor, known as "Lazy Programmer," is consistently praised for his expertise and active participation in the Q&A section, offering clear explanations and support to learners. - **Highly Recommended:** Many learners suggest that this course should be the standard for teaching SVMs, even recommending it over university courses. **Cons:** - **Pedagogical Approach:** Some learners feel that the order of concepts in the course could be improved, suggesting a better approach might be to explain the theory first and then illustrate it with code. - **Q&A Section Concerns:** A few negative remarks are directed at the Q&A section, with some learners expressing dissatisfaction with the responses or lack thereof. - **Teaching Style Preferences:** A small number of learners have indicated that they did not particularly enjoy the teaching style, citing a preference for more examples or clearer explanations. **Learner Experience:** - Many learners report a significant enhancement in their understanding of ML as well as their Python programming skills after taking this course. - The practice exercises are described as "very helpful" and contribute to the learner's ability to apply what they have learned. - Learners express appreciation for the supportive community, with some crediting the tutor's assistance for helping them overcome doubts during the course. In summary, the course is widely regarded as a valuable resource for anyone looking to understand and apply SVMs in their ML projects. While there are some areas for improvement mentioned by a few learners, the overall sentiment towards the course and its instructor is overwhelmingly positive, with many indicating they will seek out more content from the Lazy Programmer.

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2020688
udemy ID
11/11/2018
course created date
9/10/2019
course indexed date
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