Machine Learning from Scratch: A Beginner's Guide

A new beginner's guide to machine learning uses animated visuals and project-based modules to teach fundamentals like linear regression and gradient descent, hosted on the @school_whool YouTube channel. The guide aims to bridge the gap between high-level library calls and underlying math, with the creator seeking feedback on whether the animated explanations effectively clarify concepts like learning rate instability.

Machine Learning from Scratch: A Beginner's Guide Most ML tutorials fail because they either dive straight into high-level library calls without explaining the math, or they bury you in calculus until you quit. I'm taking a different approach by using animated visuals to break down the conceptual hurdles. If you can't visualize how a weight update actually shifts a decision boundary, you aren't really learning the "learning" part of machine learning. Since this is designed as a practical tutorial, the course is structured around projects rather than just slide decks. For those currently following along or looking to jump in, here is the technical trajectory of the first few modules to give you an idea of the depth: Module 1: The Fundamentals We start with the core mechanics. Instead of just saying "linear regression," we look at the actual objective function. If you're practicing this, try implementing a simple Mean Squared Error MSE function from scratch in Python to see how the penalty grows quadratically: python import numpy as np def calculate mse y true, y pred : return np.mean y true - y pred 2 Test data actual = np.array 1.0, 2.0, 3.0 predicted = np.array 1.1, 1.9, 3.2 print f"MSE: {calculate mse actual, predicted }" Module 2: Optimization and Gradient Descent This is where most beginners hit a wall. We are currently breaking down how the derivative of the cost function tells the model which direction to move. I've focused heavily on the "learning rate" problem—showing exactly what happens when your $\alpha$ is too high divergence versus too low eternal training . To get a feel for the instability of a high learning rate, you can run this snippet to see the cost explode: Simple gradient descent simulation showing divergence weight = 0.0 learning rate = 1.5 Too high target = 10.0 for i in range 5 : Simple cost: weight - target ^2 Gradient: 2 weight - target gradient = 2 weight - target weight = weight - learning rate gradient print f"Iteration {i}: weight = {weight}, cost = { weight - target 2}" The rest of the curriculum moves from these basics into more complex LLM agent concepts and deep learning architectures. I'm leveraging my experience from five different startups and academic research to strip away the fluff and focus on what actually matters for deployment in real-world AI workflows. The content is hosted over at the @school whool YouTube channel. I'm specifically looking for feedback on whether the animated explanations are clicking or if the jump from Python basics to the math is still too steep. Next Claude Code → /en/threads/2888/ All Replies （3） @CameronWizard /en/users/CameronWizard/ Spot on. I tried jumping straight to libraries and spent weeks just guessing why my weights weren't converging.