Classification Machine Learning Fundamentals

Welcome to the Classification Machine Learning Fundamentals repository! This collection of Jupyter notebooks covers essential concepts and techniques in classification machine learning. These notebooks are designed to provide you with a solid understanding of Logistic Regression, loss cost functions, and the Gradient Descent optimization algorithm.

Table of Contents

1. Logistic Regression

   • Introduction to Logistic Regression and its applications in classification
   • Implementing Logistic Regression from scratch
   • Understanding the Logistic Loss Cost Function and its role in optimization
   • Variations of Logistic Loss: Cross-Entropy, Sigmoid Cross-Entropy, and more
   • Deep dive into the Gradient Descent optimization algorithm for logistic regression
   • Practical examples and variations of Gradient Descent for logistic regression
   • The importance of feature engineering in classification tasks
   • Handling imbalanced datasets and techniques for improving model performance
   • Real-world examples and best practices in logistic regression

Getting Started

To make the most of these notebooks, ensure you have Jupyter Notebook installed on your local machine. You can install it using the following command:

pip install notebook