Logistic Regression Explained + Code From Scratch | ML Foundations Ep. 3

In this episode of the ML Foundations series, we build Logistic Regression from scratch using pure Python and NumPy — no scikit-learn, no built-in ML libraries.

We start with the basics of binary classification, understand how Logistic Regression differs from Linear Regression, and then move step-by-step into the math and implementation.

In this video, you will learn:

• What is Binary Classification
• How Logistic Regression works
• The Sigmoid Function explained clearly
• Decision Boundary concept
• Logistic Regression Cost Function (Log Loss)
• Gradient Descent for classification
• Vectorized implementation using NumPy
• Making predictions using a 0.5 threshold

We manually implement:

✔ Sigmoid function
✔ Cost function
✔ Gradient computation
✔ Gradient Descent training loop
✔ Prediction function
✔ Training and testing on sample dataset

By the end, you will fully understand how Logistic Regression works internally — from equation to working code.

Keywords:
logistic regression from scratch, logistic regression python, machine learning fundamentals, gradient descent implementation, sigmoid function tutorial, binary classification python, numpy machine learning, ML foundations series

#MachineLearning #LogisticRegression #Python #NumPy #GradientDescent #MLSeries

Видео Logistic Regression Explained + Code From Scratch | ML Foundations Ep. 3 канала Rikin Ranka