Logistic Regression

Dive into Logistic Regression, a fundamental algorithm for binary classification tasks. Learn how it models probability distributions and is widely used for applications like spam detection, medical diagnosis, and more. Explore practical implementations in Python using scikit-learn, TensorFlow, and PyTorch.

Introduction

Get introduced to Logistic Regression, a fundamental algorithm for binary classification problems in machine learning.

Theory

Understand the mathematical foundations of Logistic Regression, including the sigmoid function and likelihood estimation.

Scikit-Learn Example

Use scikit-learn to implement Logistic Regression, with a focus on simplicity and efficiency.

TensorFlow Example

Implement Logistic Regression using TensorFlow with practical code examples for binary classification.

PyTorch Example

Learn how to build and train Logistic Regression models using PyTorch in this detailed guide.

Common Mistakes & Best Practices

Discover common mistakes in Logistic Regression and learn best practices to enhance your models.

Comparison with Other Algorithms

Compare Logistic Regression with other classification algorithms to see where it excels.