Logistic Regression From Scratch Using NumPy | ML Foundation - Day 3

This video is Week 1 – Day 3 of the Machine Learning Foundations series, focused on understanding core ML algorithms from first principles.

In this lesson, we build Logistic Regression completely from scratch using NumPy — without scikit-learn, without shortcuts, and without hiding the math.
The goal is to understand classification, not just run .fit().

What this video explains in depth:
Why linear regression fails for classification problems
What the sigmoid function actually does
How probabilities emerge from linear scores
Why binary cross-entropy is the correct loss for classification
How gradient descent learns a decision boundary
The difference between probabilities and final decisions
Why thresholds matter more than people think

Every function is written manually.
Every line of code is explained slowly.
Nothing is treated as magic.

Core idea of this lesson:
Classification is not about predicting numbers.

It is about:
Computing a score
Converting it into a probability
Making a decision using a threshold
Logistic regression formalizes this process using simple, understandable math.

Who this video is for:
Aspiring machine learning and AI engineers
Software engineers revisiting ML fundamentals properly
Learners who want to understand classification, not memorize formulas
Anyone who wants to know what actually happens inside ML libraries

What this video intentionally avoids:
No scikit-learn
No abstractions
No buzzwords
No “trust the library” explanations

Frameworks like scikit-learn, PyTorch, and TensorFlow all implement this same logic.
This video shows what’s underneath.