Python short tutorials of the most common Machine Learning algorithms
This project is designed to be a quick guid to use some of the most common ML algorithms in Python. It mainly uses Scikit_learn library to implement fully functioning models on small data sets.
The project has 3 categories: 1- Regression * Linear Regression (TBA) * Multiple Regression (TBA) * Polynomial Regression (TBA) * Support Vector Regression (SVR) (TBA) * Decision Tree Regression (TBA) * Random Forest Regression (TBA)
2- Classification * Linear Classification (TBA) * K-Nearest Neighbors (K-NN) (TBA) * Support Vector Machine (SVM) (TBA) * Kernel SVM (TBA) * Naive Bayes (TBA) * Decision Tree Classification (TBA) * Random Forest Classification (TBA)
3- Clustering * K-means Clustering (TBA) * Hierarchical Clustering (TBA)
** Notes:
- The data sets used are small and designed to demonstrate the idea behind the algorithm. The code should perform similarly when applied on bigger datasets.
- Each algorithm code is written to run by itself.
- Each algorithm contains a short description, code, images of graphic results