This is my attempt to create my own machine learning library written in Python. Now it is in the earliest stage, much of what is planned has not yet been realized.
I am just tryin to learn here!
The main idea is to manually implement the algorithms of classical machine learning, i.e. supervised learning and unsupervised learning , in the hope that it will lead me to a deeper understanding of the concepts of machine learning.
Created strictly for educational purposes. In my study, I use the Internet as the main source of information for manual applications of algorithms (concepts, mathematics, and programming representation).
P.S. I am a newcomer. Any help and advice is APPRECIATED!
I repeat that I use this idea exclusively to learn, I do not follow commercial goals!
- Linear regression.
- Logistic regression.
- Support Vector Machine.
- Decision tree.
- Random forest.
- Voting classfier.
- Bagging classifer.
- Ada Boost classifier.
- Gradient Boosting classifier.
- Stacking regressor.
- Gradient Boosting regressor.
- K-neighbors classifier.
- Naive Bayes classifier.
- Perceptron.
- Multi-layer perceptron.
- Mean Squared Error.
- Mean Absolute Error.
- Medain Absolute Error.
- Mean Squared Log Error.
- Accuracy Score.
- Precision Score.
- Recall Score.
- F1 Score.
- Confusion Matrix.
- kernelz.py - kernels for SVM.
git clone https://github.com/mishazakharov/ML-library
cd ML-library
If you want to work on this together or just feeling social, feel free to contact me here. And I am also available at this([email protected]) and this([email protected]) email addresses! Feel free to give me any advice. 👍