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Development Data Science Machine Learning

Machine Learning and AI: Support Vector Machines in Python

Artificial Intelligence and Data Science Algorithms in Python for Classification and Regression
Rating: 4.6 out of 54.6 (746 ratings)
6,238 students
Created by Lazy Programmer Team, Lazy Programmer Inc.
Last updated 11/2020
English
English [Auto]
30-Day Money-Back Guarantee

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
Curated for the Udemy for Business collection

Course content

12 sections • 73 lectures • 8h 53m total length

  • Preview02:20
  • Preview04:54
  • Preview05:49
  • Where to get the code and data
    05:48

  • Beginner's Corner: Section Introduction
    05:18
  • Image Classification with SVMs
    06:00
  • Spam Detection with SVMs
    11:47
  • Medical Diagnosis with SVMs
    05:15
  • Regression with SVMs
    05:35
  • Cross-Validation
    07:20
  • How do you get the data? How do you process the data?
    05:21
  • Suggestion Box
    03:03

  • Basic Geometry
    10:51
  • Normal Vectors
    03:41
  • Logistic Regression Review
    09:45
  • Loss Function and Regularization
    04:09
  • Prediction Confidence
    07:25
  • Nonlinear Problems
    09:58
  • Linear Classifiers Section Conclusion
    04:25

  • Linear SVM Section Introduction and Outline
    03:18
  • Linear SVM Problem Setup and Definitions
    04:30
  • Margins
    08:51
  • Linear SVM Objective
    11:00
  • Linear and Quadratic Programming
    12:31
  • Slack Variables
    07:25
  • Hinge Loss (and its Relationship to Logistic Regression)
    06:22
  • Linear SVM with Gradient Descent
    03:10
  • Linear SVM with Gradient Descent (Code)
    05:06
  • Linear SVM Section Summary
    04:14

  • Duality Section Introduction
    03:43
  • Duality and Lagrangians (part 1)
    13:01
  • Lagrangian Duality (part 2)
    07:08
  • Relationship to Linear Programming
    04:19
  • Predictions and Support Vectors
    09:16
  • Why Transform Primal to Dual?
    03:26
  • Duality Section Conclusion
    02:54

  • Kernel Methods Section Introduction
    03:47
  • The Kernel Trick
    08:11
  • Polynomial Kernel
    06:06
  • Gaussian Kernel
    05:13
  • Using the Gaussian Kernel
    07:09
  • Why does the Gaussian Kernel correspond to infinite-dimensional features?
    04:39
  • Other Kernels
    07:04
  • Mercer's Condition
    06:24
  • Kernel Methods Section Summary
    02:41

  • Dual with Slack Variables
    10:40
  • Simple Approaches to Implementation
    06:25
  • SVM with Projected Gradient Descent Code
    08:19
  • Kernel SVM Gradient Descent with Primal (Theory)
    04:30
  • Kernel SVM Gradient Descent with Primal (Code)
    04:55
  • SMO (Sequential Minimal Optimization)
    09:32
  • Support Vector Regression
    05:26
  • Multiclass Classification
    04:34

  • Neural Networks Section Introduction
    02:41
  • RBF Networks
    15:38
  • RBF Approximations
    08:38
  • What Happened to Infinite Dimensionality?
    02:53
  • Build Your Own RBF Network
    03:53
  • Relationship to Deep Learning Neural Networks
    06:50
  • Neural Network-SVM Mashup
    07:15
  • Neural Networks Section Conclusion
    02:36

  • Windows-Focused Environment Setup 2018
    20:20
  • How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
    17:30

  • How to Code by Yourself (part 1)
    15:54
  • How to Code by Yourself (part 2)
    09:23
  • Proof that using Jupyter Notebook is the same as not using it
    12:29
  • Python 2 vs Python 3
    04:38

Requirements

  • Calculus, Matrix Arithmetic / Geometry, Basic Probability
  • Python and Numpy coding
  • Logistic Regression

Description

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

Instructors

Lazy Programmer Team
Artificial Intelligence and Machine Learning Engineer
Lazy Programmer Team
  • 4.6 Instructor Rating
  • 40,516 Reviews
  • 147,955 Students
  • 14 Courses

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.

I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.

Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.

I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.

My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School.

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer
Lazy Programmer Inc.
  • 4.6 Instructor Rating
  • 108,181 Reviews
  • 422,558 Students
  • 28 Courses

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.

I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.

Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.

I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.

My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. 

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

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