Machine Learning Tutorial for Beginners #8 | Gradient Descent From Scratch in Python

Machine Learning Tutorial for Beginners: Episode 8 of SyntaxPilot's free Machine Learning full course, with real-life examples for every concept. Gradient Descent From Scratch in Python. In this video you'll learn: • What a loss (cost) function is and why we square the errors • Objective and optimisation • Gradient descent: step against the gradient • Why a full step blows up (divergence) • The learning rate: too small, just right, too big • Parameters vs hyperparameters • Convergence and a stopping rule • Local vs global minimum • Saddle points Every concept starts with a real-life example, then an animation and notes, then live code in the SyntaxPilot online compiler. Practice free in the SyntaxPilot compiler (nothing to install): https://syntaxpilot-learning-2026.web... Detailed notes for this episode (code, outputs, practice): https://syntaxpilot-learning-2026.web... Chapters 00:00 Welcome 00:25 Today's agenda 00:49 The problem 01:54 The loss function 03:34 Walk downhill 04:26 The learning rate 05:51 Parameters vs hyperparameters 06:13 Convergence 06:55 Local vs global minimum 08:00 Saddle points 09:02 Common mistakes 09:25 Quick quiz 09:50 Recap and homework 10:17 Next episode Next episode: Maths for ML 7: probability basics Post your homework and quiz answer in the comments, and subscribe for the full basic-to-master playlist, free. #MachineLearning #MachineLearningTutorial #SyntaxPilot