Logistic Regression Explained + Code From Scratch | ML Foundations Ep. 3 скачать в хорошем качестве

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