Machine Learning from Scratch: A Beginner's Guide

Jamie16 Novice 1h ago Updated Jul 25, 2026 244 views 15 likes 2 min read

12 modules is the roadmap I've set for my current ML series on YouTube, and we've just hit Module 2. The goal here isn't just to skim the surface but to actually build a foundation for anyone who knows basic Python but feels lost when looking at a loss function or a gradient descent plot.

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:

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.

Help Wanted

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