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

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

What's inside

Learning objectives

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

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

Syllabus

Welcome

Introduction

Course Objectives

Course Outline

Where to get the code and data

Beginner's Corner

Beginner's Corner: Section Introduction

Image Classification with SVMs

Spam Detection with SVMs

Medical Diagnosis with SVMs

Regression with SVMs

Cross-Validation

How do you get the data? How do you process the data?

Suggestion Box

Review of Linear Classifiers

Basic Geometry

Normal Vectors

Logistic Regression Review

Loss Function and Regularization

Prediction Confidence

Nonlinear Problems

Linear Classifiers Section Conclusion

Linear SVM

Linear SVM Section Introduction and Outline

Linear SVM Problem Setup and Definitions

Margins

Linear SVM Objective

Linear and Quadratic Programming

Slack Variables

Hinge Loss (and its Relationship to Logistic Regression)

Linear SVM with Gradient Descent

Linear SVM with Gradient Descent (Code)

Linear SVM Section Summary

Duality

Duality Section Introduction

Duality and Lagrangians (part 1)

Lagrangian Duality (part 2)

Relationship to Linear Programming

Predictions and Support Vectors

Why Transform Primal to Dual?

Duality Section Conclusion

Kernel Methods

Kernel Methods Section Introduction

The Kernel Trick

Polynomial Kernel

Gaussian Kernel

Using the Gaussian Kernel

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

Other Kernels

Mercer's Condition

Kernel Methods Section Summary

Implementations and Extensions

Dual with Slack Variables

Simple Approaches to Implementation

SVM with Projected Gradient Descent Code

Kernel SVM Gradient Descent with Primal (Theory)

Kernel SVM Gradient Descent with Primal (Code)

SMO (Sequential Minimal Optimization)

Support Vector Regression

Multiclass Classification

Neural Networks (Beginner's Corner 2)

Neural Networks Section Introduction

RBF Networks

RBF Approximations

What Happened to Infinite Dimensionality?

Build Your Own RBF Network

Relationship to Deep Learning Neural Networks

Neural Network-SVM Mashup

Neural Networks Section Conclusion

Setting Up Your Environment (FAQ by Student Request)

Pre-Installation Check

Anaconda Environment Setup

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

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

How to Code by Yourself (part 1)

How to Code by Yourself (part 2)

Proof that using Jupyter Notebook is the same as not using it

Python 2 vs Python 3

Effective Learning Strategies for Machine Learning (FAQ by Student Request)

How to Succeed in this Course (Long Version)

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

Machine Learning and AI Prerequisite Roadmap (pt 1)

Machine Learning and AI Prerequisite Roadmap (pt 2)

Appendix / FAQ Finale

What is the Appendix?

BONUS

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Demystifies support vector machines for learners with no background in machine learning

Covers the theory and practical applications of support vector machines (SVMs)

Teaches learners how to implement SVMs from scratch, providing a deeper understanding of their inner workings

Provides hands-on coding exercises and real-world examples for practical implementation of SVMs

Assumes basic knowledge of calculus, matrix arithmetic, and probability, making it suitable for learners with some math background

Requires familiarity with Python coding and Numpy

Reviews summary

Intuitive learning experience

Learners say the intuitive explanations and effective exercises in this course make for an engaging learning experience.

Instructor explains theory clearly.

"I like the effort made by the instructor to explain not only the theory..."

Exercises effectively convey complex ideas.

"The structure of the course is also solid, with more complex ideas introduced progressively through exercises..."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Machine Learning and AI: Support Vector Machines in Python with these activities:

Read 'An Introduction to Statistical Learning'

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Gain a comprehensive understanding of the principles and applications of machine learning, including SVM.

View An Introduction to Statistical Learning: with... on Amazon

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Read through the book carefully, taking notes on the key concepts.
Complete the exercises and practice problems provided in the book.
Discuss the book with other students or a tutor to deepen your understanding.

Practice mathematics

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Practice basic arithmetic, algebra, and matrix operations to strengthen understanding of the mathematical operations behind SVM.

Browse courses on Calculus

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Review basic calculus concepts, such as limits, derivatives, and integrals.
Practice solving linear algebra problems involving vectors and matrices.

Join a study group for SVM

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Engage with peers to share knowledge, ask questions, and work on SVM problems together.

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Find a study group or create your own with fellow students in the course.
Meet regularly to discuss the course material, work on assignments, and prepare for exams.
Share resources, notes, and ideas with other members of the study group.

Five other activities

Expand to see all activities and additional details

Show all eight activities

Complete the SVM tutorial on Coursera

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Gain hands-on experience with SVM by working through a guided tutorial.

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Enroll in the SVM tutorial on Coursera.
Complete the video lectures and assignments in the tutorial.
Ask questions and engage in discussions with other students in the course forum.

Write a blog post

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Explain the theory behind SVM to a non-expert audience, solidifying understanding of the mathematical concepts.

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Choose a specific aspect of SVM theory to focus on, such as the hinge loss or the kernel trick.
Write a clear and concise explanation of the concept in your own words.
Provide examples and illustrations to make the concept more concrete.
Edit and refine your blog post carefully before publishing it.

Attend SVM-related conferences

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Engage with professionals and experts in the field of SVM to expand knowledge and network.

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Research and identify SVM-related conferences or meetups in your area.
Attend the conference and actively participate in sessions, workshops, and networking events.
Follow up with new connections and explore potential collaborations.

Build a portfolio of SVM projects

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Develop and showcase practical skills in SVM by creating and maintaining a portfolio of projects.

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Choose a variety of SVM projects to work on, based on your interests and career goals.
Design and implement each project carefully, documenting your work and results.
Create a portfolio website or online repository to showcase your projects.
Seek feedback on your projects from peers, mentors, or potential employers.

Contribute to open-source SVM projects

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Gain practical experience and contribute to the SVM community by participating in open-source projects.

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Identify open-source SVM projects that align with your interests and skills.
Review the project documentation and contribute bug reports or feature requests.
Submit code contributions, such as improvements or new features.
Collaborate with other developers on the project.

Career center

Learners who complete Machine Learning and AI: Support Vector Machines in Python will develop knowledge and skills that may be useful to these careers:

Data Scientist

A Data Scientist applies mathematics and statistics to extract insights from data. This course may help you build a foundation for data science by introducing you to machine learning, artificial intelligence, and support vector machines. You will learn about the theory and practice of SVMs and how to apply them to real-world problems. This course may help you pursue new career opportunities in data science, machine learning, or artificial intelligence.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

A Machine Learning Engineer designs, develops, and deploys machine learning models. This course can help you build a foundation for machine learning engineering by introducing you to support vector machines (SVMs), a powerful machine learning algorithm. You will learn about the theory and practice of SVMs and how to apply them to real-world problems. This course may help you pursue new career opportunities in machine learning engineering, data science, or artificial intelligence.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

A Data Analyst collects, analyzes, and interprets data to help businesses make better decisions. This course may help you build a foundation for data analysis by introducing you to machine learning and artificial intelligence, which are key technologies for data analysis. You will learn about the theory and practice of support vector machines (SVMs) and how to apply them to real-world problems. This course may help you pursue new career opportunities in data analysis, data science, or machine learning.

See salaries and explore the career path for Data Analyst

Software Engineer

A Software Engineer designs, develops, and maintains software applications. This course may help you build a foundation for software engineering by introducing you to machine learning and artificial intelligence, which are increasingly important technologies for software development. You will learn about the theory and practice of support vector machines (SVMs) and how to apply them to real-world problems. This course may help you pursue new career opportunities in software engineering, data science, or machine learning.

See salaries and explore the career path for Software Engineer

Quantitative Analyst

A Quantitative Analyst uses mathematical and statistical models to analyze financial data. This course may help you build a foundation for quantitative analysis by introducing you to machine learning and artificial intelligence, which are increasingly important technologies for quantitative analysis. You will learn about the theory and practice of support vector machines (SVMs) and how to apply them to real-world problems. This course may help you pursue new career opportunities in quantitative analysis, data science, or machine learning.

See salaries and explore the career path for Quantitative Analyst

Financial Analyst

A Financial Analyst analyzes financial data to make investment recommendations. This course may help you build a foundation for financial analysis by introducing you to machine learning and artificial intelligence, which are increasingly important technologies for financial analysis. You will learn about the theory and practice of support vector machines (SVMs) and how to apply them to real-world problems. This course may help you pursue new career opportunities in financial analysis, data science, or machine learning.

See salaries and explore the career path for Financial Analyst

Market Researcher

A Market Researcher studies market trends to help businesses make better decisions. This course may help you build a foundation for market research by introducing you to machine learning and artificial intelligence, which are increasingly important technologies for market research. You will learn about the theory and practice of support vector machines (SVMs) and how to apply them to real-world problems. This course may help you pursue new career opportunities in market research, data science, or machine learning.

See salaries and explore the career path for Market Researcher

Business Analyst

A Business Analyst analyzes business processes to help businesses improve their operations. This course may help you build a foundation for business analysis by introducing you to machine learning and artificial intelligence, which are increasingly important technologies for business analysis. You will learn about the theory and practice of support vector machines (SVMs) and how to apply them to real-world problems. This course may help you pursue new career opportunities in business analysis, data science, or machine learning.

See salaries and explore the career path for Business Analyst

Operations Research Analyst

An Operations Research Analyst uses mathematical and statistical models to improve the efficiency of business operations. This course may help you build a foundation for operations research by introducing you to machine learning and artificial intelligence, which are increasingly important technologies for operations research. You will learn about the theory and practice of support vector machines (SVMs) and how to apply them to real-world problems. This course may help you pursue new career opportunities in operations research, data science, or machine learning.

See salaries and explore the career path for Operations Research Analyst

Risk Analyst

A Risk Analyst analyzes risks to help businesses make better decisions. This course may help you build a foundation for risk analysis by introducing you to machine learning and artificial intelligence, which are increasingly important technologies for risk analysis. You will learn about the theory and practice of support vector machines (SVMs) and how to apply them to real-world problems. This course may help you pursue new career opportunities in risk analysis, data science, or machine learning.

See salaries and explore the career path for Risk Analyst

Actuary

An Actuary uses mathematical and statistical models to assess risk and uncertainty. This course may help you build a foundation for actuarial science by introducing you to machine learning and artificial intelligence, which are increasingly important technologies for actuarial science. You will learn about the theory and practice of support vector machines (SVMs) and how to apply them to real-world problems. This course may help you pursue new career opportunities in actuarial science, data science, or machine learning.

See salaries and explore the career path for Actuary

Statistician

A Statistician collects, analyzes, and interprets data to help businesses make better decisions. This course may help you build a foundation for statistics by introducing you to machine learning and artificial intelligence, which are increasingly important technologies for statistics. You will learn about the theory and practice of support vector machines (SVMs) and how to apply them to real-world problems. This course may help you pursue new career opportunities in statistics, data science, or machine learning.

See salaries and explore the career path for Statistician

Data Engineer

A Data Engineer builds and maintains data infrastructure. This course may help you build a foundation for data engineering by introducing you to machine learning and artificial intelligence, which are increasingly important technologies for data engineering. You will learn about the theory and practice of support vector machines (SVMs) and how to apply them to real-world problems. This course may help you pursue new career opportunities in data engineering, data science, or machine learning.

See salaries and explore the career path for Data Engineer

Computer Scientist

A Computer Scientist designs, develops, and analyzes computer systems. This course may help you build a foundation for computer science by introducing you to machine learning and artificial intelligence, which are increasingly important technologies for computer science. You will learn about the theory and practice of support vector machines (SVMs) and how to apply them to real-world problems. This course may help you pursue new career opportunities in computer science, data science, or machine learning.

See salaries and explore the career path for Computer Scientist

Teacher

A Teacher educates students in a variety of subjects. This course may be useful for teachers who want to learn more about machine learning and artificial intelligence, which are increasingly important technologies for education. You will learn about the theory and practice of support vector machines (SVMs) and how to apply them to real-world problems. This course may help you develop new teaching materials and activities for your students.

See salaries and explore the career path for Teacher