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All Projects → bamtak → machine-learning-implemetation-python

bamtak / machine-learning-implemetation-python

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Basic Machine Learning implementation with python

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Jupyter Notebook
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python
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machine-learninglinear-regressionmachine-learning-algorithmsmultinomial-naive-bayesk-means-implementation-in-pythonnewton-methodmulticlass-logistic-regressiongaussian-naive-bayes-implementationnaive-bayes-implementationperceptron-algorithmgaussian-discriminant-analysislogistic-regression-scratchmulticlass-gda-implementationwrapper-me

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Basic Machine Learning Implementation with Python and Numpy

This repository contains implementations of basic machine learning algorithms in Python and Numpy. All algorithms are implemented from scratch without using additional machine learning libraries. The intention of these notebooks is to provide a basic understanding of the algorithms and their underlying structure, not to provide the most efficient implementations.

  1. Linear Regression

  2. Logistic Regression

  3. Multi class Logisic Regression

  4. Linear Regression with newton's method

  5. Logistic Regression with newtons methods

  6. Multiclass Logistic Regression with newtons methods

  7. Perceptron

  8. Binary Naive Bayes

  9. Multinomial Naive Bayes

  10. Gaussian Naive Bayes

  11. Gaussian Discriminat Analyses

  12. KMeans

  13. Wrapper methods implementation - Forward and Backward

  14. Multiclass Gaussian Discriminat Analyses

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].