Linear classifiers (1): Basics

Linear classifiers (2): Learning parameters

Linear regression (2): Gradient descent

Machine Learning 1 - Linear Classifiers, SGD | Stanford CS221: AI (Autumn 2019)

Support Vector Machines (SVM) - the basics | simply explained

What is the K-Nearest Neighbor (KNN) Algorithm?

Founding Fathers Cold Open - SNL

Lecture 3: Linear Classifiers

Support Vector Machines Part 1 (of 3): Main Ideas!!!

Lecture 03 -The Linear Model I

VC Dimension

Artificial Intelligence & Machine learning 3 - Linear Classification | Stanford CS221 (Autumn 2021)

All Machine Learning Models Clearly Explained!

Machine Learning Foundations Course – Regression Analysis

Machine Learning 3.2 - Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA)

Naive Bayes classifier: A friendly approach

How Support Vector Machine (SVM) Works Types of SVM Linear SVM Non-Linear SVM ML DL by Mahesh Huddar

Linear Regression

Bayes Classifiers (2): Naive Bayes

Linear regression (6): Regularization