Support Vector Machine (SVM) Courses and Certifications

Support Vector Machines in R

This course will introduce the support vector machine (SVM) using an intuitive, visual approach.

• DataCamp
• 4 hours
• On-Demand
• Free Trial Available

Great Learning

Support Vector Machine - SVM - Machine Learning Tutorial

Support Vector Machines have various applications such as they can be used in classification of images, in handwritten character recognition, classification of satellite SAR data. Learn about SVM in this video by Great Learning.

• YouTube
• 39 minutes
• On-Demand
• Free Video

Support Vector Machines in Python: SVM Concepts & Code

556 ratings at Udemy

Learn Support Vector Machines in Python. Covers basic SVM models to Kernel-based advanced SVM models of Machine Learning

• Udemy
• 6 hours 20 minutes
• On-Demand
• Paid Course

Coursera

Support Vector Machines in Python, From Start to Finish

162 ratings at Coursera

Build a Support Vector Machine classifier using scikit-learn and RBF Kernel to predict heart disease. Hands-on project using UCI Machine Learning Repository data, focusing on implementation and evaluation techniques.

• Coursera
• 1 hour 55 minutes
• On-Demand
• Paid Course

SVM for Beginners: Support Vector Machines in R Studio

147 ratings at Udemy

Learn Support Vector Machines in R Studio. Basic SVM models to kernel-based advanced SVM models of Machine Learning

• Udemy
• 5 hours 10 minutes
• On-Demand
• Paid Course

Support Vector Machines

Explore Support Vector Machines, covering main concepts, polynomial and radial kernels, and practical implementation in Python.

• YouTube
• 1 hour 30 minutes
• On-Demand
• Free Video

StatQuest with Josh Starmer

Support Vector Machines Part 1 - Main Ideas

Demystifying Support Vector Machines: Learn key concepts, from maximal margin classifiers to kernel functions, in this comprehensive introduction to SVM fundamentals and applications.

• YouTube
• 21 minutes
• On-Demand
• Free Video

Serrano.Academy

Support Vector Machines - A Friendly Introduction

Explore Support Vector Machines through visual explanations, covering key concepts like data separation, perceptron algorithm, classification errors, and the C parameter for optimal model performance.

• YouTube
• 31 minutes
• On-Demand
• Free Video

StatQuest with Josh Starmer

Support Vector Machines in Python from Start to Finish

Comprehensive guide to implementing Support Vector Machines in Python, covering data preparation, model building, and optimization techniques for machine learning practitioners.

• YouTube
• 45 minutes
• On-Demand
• Free Video

Krish Naik

SVM Kernels In-Depth Intuition - Polynomial Kernels Part 3 - Machine Learning Data Science

Explore polynomial kernels in SVM, understanding their intuition, use cases, and hyperparameter tuning for enhanced machine learning model performance.

• YouTube
• 21 minutes
• On-Demand
• Free Video

Great Learning

Support Vector Machines

Comprehensive introduction to Support Vector Machines, covering theory, kernel functions, advantages, applications, and practical implementation in Python for machine learning enthusiasts.

• YouTube
• 1 hour
• On-Demand
• Free Video

Pascal Poupart

Support Vector Machines - Continued

Explore advanced concepts of support vector machines, including soft margins, slack variables, and multi-class classification techniques.

• YouTube
• 29 minutes
• On-Demand
• Free Video

Pascal Poupart

Support Vector Machines

Learn about support vector machines, margin classifiers, and linear separators. Explore dual representation, Lagrangian optimization, and practical applications in classification problems.

• YouTube
• 1 hour 18 minutes
• On-Demand
• Free Video

Hausdorff Center for Mathematics

Non Asymptotic Analysis of L1 Support Vector Machines

Explores non-asymptotic analysis of l1 support vector machines for sparse classification in high-dimensional data, providing error guarantees applicable to real-world scenarios.

• YouTube
• 48 minutes
• On-Demand
• Free Video

Machine Learning & Deep Learning in Python & R

5981 ratings at Udemy

Covers Regression, Decision Trees, SVM, Neural Networks, CNN, Time Series Forecasting and more using both Python & R

• Udemy
• 1 day 9 hours 12 minutes
• On-Demand
• Paid Course