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)
- Kernel PCA
- Linear Discriminant Analysis (LDA)
- Binary Cross Entropy
- Categorical Crossentropy
- Accuracy Score
- Confusion Matrix
- Precision
- Recall
- F1-Score
- Receiver operating characteristic (ROC)
- Area under the ROC curve (AUC)
- Hinge Loss
- KL Divergence
- Brier Score
- Mean Squared Error
- Mean Squared Logaritmic Error
- Mean Absolute Error
- Mean Absolute Percentage Error
- Median Absolute Error
- Cosine Similartiy
- R2 Score
- Tweedie Deviance
- D^2 Score
- Huber loss
- Log Cosh Loss
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.