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.ipynb_checkpoints
.ipynb_checkpoints
 
 
README_files
README_files
 
 
figures
figures
 
 
BayesianClassifier.ipynb
BayesianClassifier.ipynb
 
 
Binary Naive Bayes.ipynb
Binary Naive Bayes.ipynb
 
 
Gaussian Discriminant Analyses.ipynb
Gaussian Discriminant Analyses.ipynb
 
 
Gaussian Naive Bayes.ipynb
Gaussian Naive Bayes.ipynb
 
 
ID3.ipynb
ID3.ipynb
 
 
KMeans.ipynb
KMeans.ipynb
 
 
Linear Regression Implementation from scratch.ipynb
Linear Regression Implementation from scratch.ipynb
 
 
Linear Regression with Newtons Method.ipynb
Linear Regression with Newtons Method.ipynb
 
 
Linear_reg.ipynb
Linear_reg.ipynb
 
 
Logistic Regression with Newtons Method.ipynb
Logistic Regression with Newtons Method.ipynb
 
 
Logistic Regression.ipynb
Logistic Regression.ipynb
 
 
Momentum in ML.ipynb
Momentum in ML.ipynb
 
 
Multi Class Gaussian Discriminant Analyses.ipynb
Multi Class Gaussian Discriminant Analyses.ipynb
 
 
Multi Class Logistic Regression with Newtons Method.ipynb
Multi Class Logistic Regression with Newtons Method.ipynb
 
 
Multi Class Logistic Regression.ipynb
Multi Class Logistic Regression.ipynb
 
 
Multinomial Naive Bayes.ipynb
Multinomial Naive Bayes.ipynb
 
 
Naive Bayes Implementation.ipynb
Naive Bayes Implementation.ipynb
 
 
Perceptron.ipynb
Perceptron.ipynb
 
 
README.md
README.md
 
 
Wrapper Method For Feature Selection - Forward and Backward .ipynb
Wrapper Method For Feature Selection - Forward and Backward .ipynb
 
 
book_rec_knn.ipynb
book_rec_knn.ipynb
 
 
gaussian_naive_bayes.py
gaussian_naive_bayes.py
 
 
gda.py
gda.py
 
 
kmeans.ipynb
kmeans.ipynb
 
 
kmeans.py
kmeans.py
 
 
lwlr.ipynb
lwlr.ipynb
 
 
naive_bayes.py
naive_bayes.py
 
 
spam filter.ipynb
spam filter.ipynb
 
 
svm.ipynb
svm.ipynb
 
 
train_test_split.pyc
train_test_split.pyc
 
 
utils.py
utils.py
 
 
utils.pyc
utils.pyc
 
 

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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

  15. SVM

  16. Spam Filter

  17. Linear Weighted Regression

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