Gradient Descent in Logistic Regression (Step-by-Step Explanation + Python Code)
In this video, we explain Gradient Descent in Logistic Regression, one of the most important concepts in Machine Learning. If you want to understand how machine learning models learn from data, this tutorial will help you understand the gradient descent optimization algorithm step-by-step.
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We start with a simple introduction to Logistic Regression, then explain the cost function, loss function, and why gradient descent is necessary to minimize error in machine learning models. You will also learn how the learning rate works, how gradients update model parameters, and how this algorithm finds the optimal weights and bias.
This tutorial also includes a basic Python implementation of logistic regression using gradient descent, so beginners can clearly understand the concept with practical code.
Topics Covered in this video:
Logistic Regression explained
What is Gradient Descent?
Cost Function and Loss Function
How Gradient Descent works
Learning Rate explained
Gradient calculation
Python implementation of Logistic Regression
Advantages and limitations of Gradient Descent
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