Kernel Methods Courses and Certifications

Krish Naik

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

Understand how polynomial SVM kernels transform features into higher dimensions so nonlinearly separable classes can be divided by a hyperplane.

• YouTube
• 21 minutes
• Self-Paced
• Free Video

Stanford CS229 - Machine Learning - Summer 2019 - Kernel Methods & Support Vector Machine

Explore kernel methods and support vector machines in a lecture from a machine-learning course.

• YouTube
• 1 hour 55 minutes
• Self-Paced
• Free Video

Deep Learning Essentials

10 ratings at Coursera

Trace deep learning from the perceptron to backpropagation, covering stochastic gradient descent, kernel methods, and fully connected networks with short Python coding assignments.

• Coursera
• 4 weeks, 4 hours a week
• Self-Paced
• Paid Course

Support Vector Machines

Explores support vector machines through polynomial and radial basis function kernels, then implements an SVM workflow in Python.

• YouTube
• 1 hour 30 minutes
• Self-Paced
• Free Video

StatQuest with Josh Starmer

Support Vector Machines Part 1 - Main Ideas

Explains how support vector machines classify data using maximal margins, soft margins, polynomial and radial basis function kernels, and the kernel trick.

• YouTube
• 21 minutes
• Self-Paced
• Free Video

Andreas Geiger

Neural Radiance Fields for View Synthesis

Explores Neural Radiance Fields for synthesizing photorealistic novel views from sparse images using continuous neural scene representations and volumetric rendering.

• YouTube
• 49 minutes
• Self-Paced
• Free Video

Great Learning

Support Vector Machines

Learn how support vector machines separate nonlinear data with kernel functions, including their advantages, applications, and a practical Python demonstration.

• YouTube
• 1 hour
• Self-Paced
• Free Video

Richard McElreath

Statistical Rethinking - Gaussian Processes - Lecture B06

Explore Gaussian processes for flexible non-parametric modeling and spatial correlation analysis in Bayesian statistics.

• YouTube
• 1 hour
• Self-Paced
• Free Video

Google TechTalks

Fast Neural Kernel Embeddings for General Activations

Explore fast sketching methods for approximating multi-layered NNGP and NTK matrices with various activation functions, overcoming limitations of prior works and achieving significant speedups.

• YouTube
• 34 minutes
• Self-Paced
• Free Video

Probabilistic AI School

Gaussian Processes

Explore Gaussian Processes with Daniel Hernández-Lobato, covering key concepts, applications, and advanced techniques in probabilistic machine learning.

• YouTube
• 2 hours 22 minutes
• Self-Paced
• Free Video

UofU Data Science

Support Vector Machines and Regularized Risk Minimization - Lecture 21

Delve into advanced support vector machine concepts and regularized risk minimization principles for enhanced machine learning model development.

• YouTube
• 1 hour 21 minutes
• Self-Paced
• Free Video

UofU Data Science

Support Vector Machines: Theory and Applications - Lecture 21

Dive into advanced concepts of Support Vector Machines (SVMs), exploring their mathematical foundations and practical applications in machine learning classification problems.

• YouTube
• 57 minutes
• Self-Paced
• Free Video

Apprenez à entraîner des modèles supervisés non linéaires avec des méthodes à noyaux, des SVM, la régression ridge et des réseaux de neurones simples.

• OpenClassrooms
• 12 hours
• Self-Paced
• Free Certificate