Linear Regression with Multiple Variables | ML-005 Lecture 4 | Stanford University | Andrew Ng

Contents:
Multiple Features,
Gradient Descent for Multiple Variables,
Gradient Descent in Practice - Part 1 - Feature Scaling,
Gradient Descent in Practice - Part 2 - Learning Rate,
Features and Polynomial Regression,
Normal Equation,
Normal Equation Noninvertibility Optional,

[@Phi_AI]

This is implementation of Linear regression from scratch in NumPy only. In-depth explanation of key concepts like Cost Function and Gradient Descent kzbin.info/www/bejne/rammgquQgNRnnrc

[@jinli1835]

It is a bit confusing when it comes to the design matrix

[@abdalrahmanessam6710]

what you did not understand?