ML-Classification

This repository documents my progress while learning Machine Learning with Python: Classification at Alura. Each notebook corresponds to a specific topic in the Machine Learning course I'm taking.

Notebooks

1. 01-Multiclass-Problems.ipynb

   • How to handle multiclass classification problems using "one-vs-all" and "one-vs-one" strategies.

2. 02-Model-Optimization.ipynb

   • Model evaluation techniques such as cross-validation and ROC curve analysis.

3. 03-Feature-Selection.ipynb

   • Feature selection methods, including LASSO and Recursive Feature Elimination (RFE).

4. 04-Combining-Classifiers.ipynb

   • Ensemble learning techniques like Random Forest and Gradient Boosting to enhance model performance.

5. 05-XGBoost-Classification.ipynb

   • Using XGBoost to solve classification problems, tuning of hiper-params and performance analysis

6. 06-Semi-Supervised.ipynb

   • Introduction to semi-supervised learning by solving classification problems

About the Course

This course covers various aspects of Machine Learning, including supervised and semi-supervised methods, data preprocessing, model tuning, and more.