Gradient Descent Explained Step-by-Step | Iterative Parameter Estimation in Machine Learning

Gradient Descent is one of the most important optimization algorithms used in machine learning for estimating model parameters. In this video, we explain iterative parameter estimation using gradient descent and show how it minimizes the loss function in linear regression. You will learn how gradient descent updates model parameters (β coefficients) step-by-step to reduce prediction error using the Mean Squared Error (MSE) cost function. This tutorial covers the mathematical intuition, algorithm steps, and practical understanding of gradient descent. 📌 Topics Covered What is Gradient Descent? Iterative Parameter Estimation Loss Function (Mean Squared Error) Gradient Calculation using Derivatives Learning Rate (Alpha) Convergence of the Algorithm Gradient Descent for Linear Regression Step-by-Step Example This video is perfect for students learning Machine Learning, Data Science, Artificial Intelligence, and Statistical Modeling. 🎯 Who Should Watch Machine Learning Students Data Science Beginners AI Enthusiasts Statistics & Analytics Learners What is Gradient Descent in Machine Learning? Gradient descent is an iterative optimization algorithm used to minimize a loss function by updating model parameters in the direction of the steepest decrease. Why is Gradient Descent used in Linear Regression? It helps estimate regression coefficients by minimizing the mean squared error between predicted and actual values. What is the Learning Rate in Gradient Descent? The learning rate (α) is a hyperparameter that determines how large the update step should be during each iteration. When does Gradient Descent converge? The algorithm converges when parameter updates become very small and the loss function reaches a minimum. #MachineLearning #GradientDescent #DataScience #AI #LinearRegression