README.md

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

15. SVM

16. Spam Filter

17. Linear Weighted Regression