This repository contains explanations and implementations of machine learning algorithms and concepts. The explanations are also available as articles on my website.
- Linear Regression
- Logistic Regression
- K Nearest Neighbors
- Decision Tree
- KMeans
- Mean Shift
- DBSCAN
- Random Forest
- Adaboost
- Gradient Boosting
- Principal Component Analysis (PCA)
- Binary Cross Entropy
- Accuracy Score
- Hinge Loss
- Mean Squared Error
- Mean Squared Logaritmic Error
- Mean Absolute Error
- Mean Absolute Percentage Error
- Median Absolute Error
- Cosine Similartiy
- R2 Score
- Tweedie Deviance
- Huber loss
- Log Cosh Loss
- KL Divergence
Contributions to Machine-Learning-Explained are always welcome, whether code or documentation changes. For contribution guidelines, please see the CONTRIBUTING.md file.
This project is licensed under the MIT License - see the LICENSE.md file for details.
