Machine Learning and AI: Support Vector Machines in Python

Machine Learning and AI: Support Vector Machines in Python, available at $69.99, has an average rating of 4.64, with 74 lectures, based on 1742 reviews, and has 27947 subscribers.

You will learn about 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 This course is ideal for individuals who are Beginners who want to know how to use the SVM for practical problems or Experts who want to know all the theory behind the SVM or Professionals who want to know how to effectively tune the SVM for their application It is particularly useful for Beginners who want to know how to use the SVM for practical problems or Experts who want to know all the theory behind the SVM or Professionals who want to know how to effectively tune the SVM for their application.

Enroll now: Machine Learning and AI: Support Vector Machines in Python

Summary

Title: Machine Learning and AI: Support Vector Machines in Python

Price: $69.99

Average Rating: 4.64

Number of Lectures: 74

Number of Published Lectures: 74

Number of Curriculum Items: 74

Number of Published Curriculum Objects: 74

Original Price: $24.99

Quality Status: approved

Status: Live

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

Who Should Attend

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

Target Audiences

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

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

Course Curriculum

Chapter 1: Welcome

Lecture 1: Introduction

Lecture 2: Course Objectives

Lecture 3: Course Outline

Lecture 4: Where to get the code and data

Chapter 2: Beginner's Corner

Lecture 1: Beginner's Corner: Section Introduction

Lecture 2: Image Classification with SVMs

Lecture 3: Spam Detection with SVMs

Lecture 4: Medical Diagnosis with SVMs

Lecture 5: Regression with SVMs

Lecture 6: Cross-Validation

Lecture 7: How do you get the data? How do you process the data?

Lecture 8: Suggestion Box

Chapter 3: Review of Linear Classifiers

Lecture 1: Basic Geometry

Lecture 2: Normal Vectors

Lecture 3: Logistic Regression Review

Lecture 4: Loss Function and Regularization

Lecture 5: Prediction Confidence

Lecture 6: Nonlinear Problems

Lecture 7: Linear Classifiers Section Conclusion

Chapter 4: Linear SVM

Lecture 1: Linear SVM Section Introduction and Outline

Lecture 2: Linear SVM Problem Setup and Definitions

Lecture 3: Margins

Lecture 4: Linear SVM Objective

Lecture 5: Linear and Quadratic Programming

Lecture 6: Slack Variables

Lecture 7: Hinge Loss (and its Relationship to Logistic Regression)

Lecture 8: Linear SVM with Gradient Descent

Lecture 9: Linear SVM with Gradient Descent (Code)

Lecture 10: Linear SVM Section Summary

Chapter 5: Duality

Lecture 1: Duality Section Introduction

Lecture 2: Duality and Lagrangians (part 1)

Lecture 3: Lagrangian Duality (part 2)

Lecture 4: Relationship to Linear Programming

Lecture 5: Predictions and Support Vectors

Lecture 6: Why Transform Primal to Dual?

Lecture 7: Duality Section Conclusion

Chapter 6: Kernel Methods

Lecture 1: Kernel Methods Section Introduction

Lecture 2: The Kernel Trick

Lecture 3: Polynomial Kernel

Lecture 4: Gaussian Kernel

Lecture 5: Using the Gaussian Kernel

Lecture 6: Why does the Gaussian Kernel correspond to infinite-dimensional features?

Lecture 7: Other Kernels

Lecture 8: Mercer's Condition

Lecture 9: Kernel Methods Section Summary

Chapter 7: Implementations and Extensions

Lecture 1: Dual with Slack Variables

Lecture 2: Simple Approaches to Implementation

Lecture 3: SVM with Projected Gradient Descent Code

Lecture 4: Kernel SVM Gradient Descent with Primal (Theory)

Lecture 5: Kernel SVM Gradient Descent with Primal (Code)

Lecture 6: SMO (Sequential Minimal Optimization)

Lecture 7: Support Vector Regression

Lecture 8: Multiclass Classification

Chapter 8: Neural Networks (Beginner's Corner 2)

Lecture 1: Neural Networks Section Introduction

Lecture 2: RBF Networks

Lecture 3: RBF Approximations

Lecture 4: What Happened to Infinite Dimensionality?

Lecture 5: Build Your Own RBF Network

Lecture 6: Relationship to Deep Learning Neural Networks

Lecture 7: Neural Network-SVM Mashup

Lecture 8: Neural Networks Section Conclusion

Chapter 9: Setting Up Your Environment (FAQ by Student Request)

Lecture 1: Pre-Installation Check

Lecture 2: Anaconda Environment Setup

Lecture 3: How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

Chapter 10: Extra Help With Python Coding for Beginners (FAQ by Student Request)

Lecture 1: How to Code by Yourself (part 1)

Lecture 2: How to Code by Yourself (part 2)

Lecture 3: Proof that using Jupyter Notebook is the same as not using it

Lecture 4: Python 2 vs Python 3

Chapter 11: Effective Learning Strategies for Machine Learning (FAQ by Student Request)

Lecture 1: How to Succeed in this Course (Long Version)

Lecture 2: Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?

Lecture 3: Machine Learning and AI Prerequisite Roadmap (pt 1)

Lecture 4: Machine Learning and AI Prerequisite Roadmap (pt 2)

Chapter 12: Appendix / FAQ Finale

Lecture 1: What is the Appendix?

Lecture 2: BONUS

Instructors

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer

Rating Distribution

1 stars: 9 votes
2 stars: 8 votes
3 stars: 35 votes
4 stars: 789 votes
5 stars: 901 votes

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