Perceptron Courses and Certifications

Great Learning

Multilayer Perceptron in Neural Networks - Machine Learning Tutorial

Learn how multilayer perceptrons use interconnected neuron layers to model complex data patterns, with an introduction to neural networks and a practical demonstration.

• YouTube
• 2 hours 7 minutes
• Self-Paced
• Free Video

Donato Capitella

Training MNIST Perceptron with PyTorch - Lab 3.4

Master PyTorch implementation of MNIST digit recognition through hands-on training, including data preparation, model development, and performance optimization techniques.

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

UofU Data Science

Perceptron Algorithm and Mistake Bound Theorem - Lecture 9

Dive into advanced Perceptron concepts, exploring practical algorithm variants and the mistake bound theorem for enhanced machine learning understanding.

• YouTube
• 1 hour 18 minutes
• Self-Paced
• Free Video

UofU Data Science

Perceptron Mistake Bound Theorem in Machine Learning - Lecture 10

Dive into the mathematical foundations of Perceptron algorithms, exploring the theorem and proof behind mistake bounds in machine learning classification.

• YouTube
• 1 hour 23 minutes
• Self-Paced
• Free Video

UofU Data Science

The Perceptron Algorithm for Linear Classification - Lecture 26

Dive into the Perceptron Algorithm for linear classification, exploring perfect classifiers, margin concepts, and max-margin classifiers while understanding their practical applications.

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

UofU Data Science

Introduction to the Perceptron Algorithm - Lecture 8

Dive into the fundamental concepts of the Perceptron algorithm, exploring its core mechanisms and applications in neural network foundations.

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

Pascal Poupart

Introduction to Machine Learning - Spring 2019 - University of Waterloo

Dive into comprehensive machine learning fundamentals covering neural networks, SVMs, deep learning, CNNs, transformers, and ensemble methods from University of Waterloo.

• YouTube
• 1 day 4 hours 26 minutes
• Self-Paced
• Free Video

Data Science Courses

Machine Learning and Classification - STAT 441/841 CM 763 - Fall 2015

Master classification algorithms from linear discriminants to neural networks, covering PCA, SVM, decision trees, boosting, and PAC learning theory.

• YouTube
• 20 hours 42 minutes
• Self-Paced
• Free Video

AssemblyAI

Machine Learning From Scratch

Master 10 essential ML algorithms by building KNN, Linear/Logistic Regression, Decision Trees, Random Forest, Naive Bayes, PCA, Perceptron, SVM, and K-Means from scratch in Python.

• YouTube
• 2 hours 52 minutes
• Self-Paced
• Free Video

Patrick Loeber

Machine Learning from Scratch - Python Tutorials

Master machine learning algorithms by building KNN, regression, SVM, decision trees, clustering, and more from scratch using Python without external libraries.

• YouTube
• 4 hours 53 minutes
• Self-Paced
• Free Video

Alfredo Canziani

NYU Artificial Intelligence - Introduction to Machine Learning Fundamentals

Discover fundamental AI concepts including Naïve Bayes classification, perceptron algorithms, logistic regression, and optimization techniques through NYU's comprehensive curriculum.

• YouTube
• 6 hours 11 minutes
• Self-Paced
• Free Video

UofU Data Science

Machine Learning - Spring 2024

Master fundamental ML algorithms from decision trees to neural networks, covering theory, implementation, and practical applications for real-world problem solving.

• YouTube
• 1 day 11 hours 22 minutes
• Self-Paced
• Free Video

UofU Data Science

Machine Learning - Spring 2023

Dive into comprehensive machine learning fundamentals covering supervised learning, decision trees, linear models, perceptron, SVMs, neural networks, and Bayesian methods.

• YouTube
• 1 day 13 hours 23 minutes
• Self-Paced
• Free Video

Alexander Amini

MIT Introduction to Deep Learning - 6.S191 - Lecture 1

Dive into the foundations of deep learning with MIT's comprehensive lecture series, exploring core concepts and principles with Alexander Amini.

• YouTube
• 1 hour 9 minutes
• Self-Paced
• Free Video

Serrano.Academy

Support Vector Machines - A Friendly Introduction

A visual, low-math introduction to SVMs that builds a maximum-margin classifier from the perceptron algorithm and explains how the C parameter balances errors.

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