aserehal/machine-learning-explained

Learn the theory, math and code behind different machine learning algorithms and techniques.

Machine-Learning-Explained

This repository contains explanations and implementations of machine learning algorithms and concepts. The explanations are also available as articles on my website.

Machine Learning Algorithms

• 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)

Optimizers

• Gradient Descent
• Adagrad
• Adadelta
• RMSprop
• Adam
• AdaMax
• Nadam
• AMSGrad
• AdamW
• QHM
• QHAdam
• RAdam

Activation Functions

• ELU
• GELU
• Leaky RELU
• Mish
• RELU
• SELU
• Sigmoid
• SILU
• Softmax
• Softplus
• Tanh

Metrics

• 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

Ensemble Methods

• Averaging
• Bagging
• Blending
• Majority Vote
• Stacking
• Stacking retrained
• Weighted Average

Contributing

Contributions to Machine-Learning-Explained are always welcome, whether code or documentation changes. For contribution guidelines, please see the CONTRIBUTING.md file.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.