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Machine Learning algorithms implemented from scratch
| Date | Stars |
|---|---|
| 2026-07-31 | 520 |
| 2026-08-06 | 520 |
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# Synopsis This repository contains popular Machine Learning algorithms, which have been introduced in various blog posts (http://ataspinar.com). Most of the algorithms are accompanied with blog-posts in which I try to explain the mathematics behind and the interpretation of these algorithms. # Motivation Machine Learning is fun! But more importantly, Machine Learning is easy. But the academic literature or even (wikipedia-pages) is full with unnecessary complicated terminology, notation and formulae. This gives people the idea that these ML algorithms can only be understood with a full understanding of advanced math and statistics. Stripped from all of these superfluous language we are left with simple maths which can be expressed in a few lines of code. # Notebooks explaining the mathematics I have also provided some notebooks, explaining the mathematics of some Machine Learning algorithms. + [Linear Regression and Logistic Regression](https://github.com/taspinar/siml/blob/master/notebooks/Linear%20Regression%2C%20Logistic%20Regression.ipynb) + [Naive Bayes Classification](https://github.com/taspinar/siml/blob/master/notebooks/Naive_Bayes.ipynb) + [Perceptron Classification](https://github.com/taspinar/siml/blob/master/notebooks/Perceptron.ipynb) + [Classification with Scikit-Learn](https://github.com/taspinar/siml/blob/master/notebooks/scikit_classification.ipynb) + [Machine Learning with Signal Analysis techniques](https://github.com/taspinar/siml/blob/master/notebooks/Machine%20Learning%20with%20Signal%20Processing%20techniques.ipynb) + [Time-series forecasting with signal analysis techniques](https://github.com/taspinar/siml/blob/master/notebooks/Time%20Series%20forecasting%20with%20Stochastic%20Signal%20Analysis.ipynb) # Notebooks explaining Machine Learning with the Wavelet Transform + [Introduction to PyWavelets (for Wavelet Analysis](https://github.com/taspinar/siml/blob/master/notebooks/WV1%20-%20Using%20PyWavelets%20for%20Wavelet%20Analysis.ipynb) + [Using Wavelets to Visualize the Scaleogram, time-axis and Fourier Transform](https://github.com/taspinar/siml/blob/master/notebooks/WV2%20-%20Visualizing%20the%20Scaleogram%2C%20time-axis%20and%20Fourier%20Transform.ipynb) + [Classification of signals using the Continuous Wavelet Transform and Convolutional Neural Networks](https://github.com/taspinar/siml/blob/master/notebooks/WV3%20-%20Classification%20of%20signals%20using%20the%20CWT%20and%20CNN.ipynb) + [Classification of ECG signals using the Discrete Wavelet Transform and Gradient Boosting](https://github.com/taspinar/siml/blob/master/notebooks/WV4%20-%20Classification%20of%20ECG%20signals%20using%20the%20Discrete%20Wavelet%20Transform.ipynb) + [Classification of signals using the Discrete Wavelet Transform and several classifiers](https://github.com/taspinar/siml/blob/master/notebooks/WV5%20-%20Classification%20of%20the%20UCI-HAR%20dataset%20using%20Discrete%20Wavelet%20Transform..ipynb) # Installation To install **siML**: ```python (sudo) pip install siml ``` or you can clone the repository and in the folder containing setup.py ```python python setup.py install ``` # Code Example TODO
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matched fp:ab287c91396129ed, llm:Repository description: 'Machine Learning algorithms implemented from scratch'
matched fp:ab287c91396129ed, llm:Repository description: 'Machine Learning algorithms implemented from scratch'