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In this repository, I have included the ipynb files that have both scratch and sklearn implementation of ML algorithms with detailed explanation of the algorithm.

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AUROC_Detail.ipynb
AUROC_Detail.ipynb
 
 
Confusion_matrix.ipynb
Confusion_matrix.ipynb
 
 
Decision_tree.ipynb
Decision_tree.ipynb
 
 
Decision_tree_scratch.ipynb
Decision_tree_scratch.ipynb
 
 
GradientDescent_mathematical_formulation.ipynb
GradientDescent_mathematical_formulation.ipynb
 
 
GradientDescent_scratch_for_multi_linearRegression.ipynb
GradientDescent_scratch_for_multi_linearRegression.ipynb
 
 
Logistic_Regression_with_a_Neural_Network_mindset.ipynb
Logistic_Regression_with_a_Neural_Network_mindset.ipynb
 
 
Multiple_linear_regression_sklearn.ipynb
Multiple_linear_regression_sklearn.ipynb
 
 
Polynomial_Regression.ipynb
Polynomial_Regression.ipynb
 
 
RandomForest.ipynb
RandomForest.ipynb
 
 
RegressionPerformanceMatrics.ipynb
RegressionPerformanceMatrics.ipynb
 
 
Regularization_Ridge_lasso.ipynb
Regularization_Ridge_lasso.ipynb
 
 
Simple_linear_regression_scratch.ipynb
Simple_linear_regression_scratch.ipynb
 
 
Simple_linear_regression_sklearn.ipynb
Simple_linear_regression_sklearn.ipynb
 
 
Unsupervised_learning.ipynb
Unsupervised_learning.ipynb
 
 
Vectorization.ipynb
Vectorization.ipynb
 
 
sklearn_different_modelselection.ipynb
sklearn_different_modelselection.ipynb
 
 

About

In this repository, I have included the ipynb files that have both scratch and sklearn implementation of ML algorithms with detailed explanation of the algorithm.

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0 stars

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1 watching

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