Goose Machine Learning

A very self-learning machine learning project.

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Note about this project

• Old project: This repo
• Feel free to use it (in that case, please credit me for my effort).
• The program still has bugs and is not user-friendly.

Topics:

Supervised Learning

Model name	Progress	Note
Linear Regression	Done
Polynomial Regression	Done	Use PLA or Neural Network
Logistic Regression	Done
Softmax Regression	Done	Not including Neural Network
Other Regressions	-
Neural Network	Half
Decision Tree	Done*
Naive Bayes	Done*
Support Vector Machine	Half
Discriminant Analysis	-
K-Nearest Neighbors	Done
Random Forest	Done*

Unsupervised Learning

Model name	Progress	Path
K-Means Clustering	Done
K-Medroids Clustering	-
Hierarchical Clustering	-
Principal Component Analysis	Done	Bugs
Linear Component Analysis	Done	Bugs
Singular Value Decomposition	-

Optimizers

Optimizers	Progress
Gradient Descent	Done
Stochastic Gradient Descent	-
Batch Gradient Descent	-
Adagrad	-
Adadelta	-
RMSprop	-
Adam	-

Others

Model name	Progress	Path
Read from input data	Half	Available datasets: iris, breast cancer, wine
Create data	Half	Available generators: linear, group and spiral
Save data	-	-
Load data	-	-
Edit data	-	-
Normalise data	-	-
Balance data	-	-
Data Inappropriate Warning	-	-
Graph drawing	Done	Still a little confused
Other Frameworks Comparisons	-	-

Screenshot

Coming soon

References:

Inspiration: Machine Learning from scratch
medium.com
Vietnamese machine learning from scratch