Machine Learning Basics | Logistic Regression Mathematics

📊 In this tutorial, we explore the mathematics behind Logistic Regression, one of the most important algorithms in machine learning and statistics. ✅ What You’ll Learn: Introduction to Logistic Regression Linear regression vs. logistic regression The Sigmoid Function and probability interpretation Hypothesis representation in logistic regression Cost function for classification Gradient Descent for logistic regression optimization Mathematical intuition behind decision boundaries Hands-on coding with Python & NumPy 🎓 This session is part of the SMIT Data Science & Machine Learning Series, designed for beginners and intermediate learners. 👉 By the end of this video, you’ll clearly understand the mathematical foundations of Logistic Regression and how it’s applied in classification problems. 🔔 Subscribe for more videos on Regression, Classification, and Machine Learning algorithms.