@@ -68,8 +68,9 @@ W,b =clf.getWeight()
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### Code and examples are [ here] ( https://github.com/Nikeshbajaj/DeepLearning_from_scratch )
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#### Full detail of implementation and use of code is describe [ here] ( https://github.com/Nikeshbajaj/DeepLearning_from_scratch )
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- [ <img src =" https://raw.githubusercontent.com/Nikeshbajaj/utis/master/images/jp3.png " height =" 50 " />] ( https://github.com/Nikeshbajaj/DeepLearning_from_scratch/blob/master/AllExamples.ipynb )
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-   ;  ;  ;  ;  ;  ;  ;  ;  ;  ; [ <img src =" https://raw.githubusercontent.com/Nikeshbajaj/utis/master/images/jp.png " height =" 50 " />] ( https://github.com/Nikeshbajaj/DeepLearning_from_scratch/raw/master/AllExamples.ipynb )
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+ [ <img src =" https://raw.githubusercontent.com/Nikeshbajaj/utis/master/images/jp3.png " height =" 50 " />] ( https://nbviewer.jupyter.org/github/nikeshbajaj/DeepLearning_from_scratch/blob/master/AllExamples.ipynb )
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+   ;  ;  ;  ;  ;  ;  ;  ;  ;  ;
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+ [ <img src =" https://raw.githubusercontent.com/Nikeshbajaj/utis/master/images/jp.png " height =" 50 " />] ( https://raw.githubusercontent.com/nikeshbajaj/DeepLearning_from_scratch/master/AllExamples.ipynb )
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@@ -160,8 +161,8 @@ Classifier based on Bayes rule:
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Notebook include example of Iris data, Breast Cancer and Digit classification (MNIST)
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### Notebook
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- [ <img src =" https://raw.githubusercontent.com/Nikeshbajaj/utis/master/images/jp3.png " height =" 50 " />] ( https://github.com /Nikeshbajaj/Machine_Learning_From_Scratch/blob/master/Probabilistic/NaiveBayes_example.ipynb )
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-   ;  ;  ;  ;  ;  ;  ;  ;  ;  ; [ <img src =" https://raw.githubusercontent.com/Nikeshbajaj/utis/master/images/jp.png " height =" 50 " />] ( https://github. com/Nikeshbajaj/Machine_Learning_From_Scratch/raw /master/Probabilistic/NaiveBayes_example.ipynb )
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+ [ <img src =" https://raw.githubusercontent.com/Nikeshbajaj/utis/master/images/jp3.png " height =" 50 " />] ( https://nbviewer.jupyter.org/github /Nikeshbajaj/Machine_Learning_From_Scratch/blob/master/Probabilistic/NaiveBayes_example.ipynb )
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+   ;  ;  ;  ;  ;  ;  ;  ;  ;  ; [ <img src =" https://raw.githubusercontent.com/Nikeshbajaj/utis/master/images/jp.png " height =" 50 " />] ( https://raw.githubusercontent. com/Nikeshbajaj/Machine_Learning_From_Scratch/master/Probabilistic/NaiveBayes_example.ipynb )
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  ;  ;  ;  ;  ;  ;  ;  ;  ;  ; [ <img src =" https://raw.githubusercontent.com/Nikeshbajaj/utis/master/images/py.png " height =" 50 " />] ( https://github.com/Nikeshbajaj/Machine_Learning_From_Scratch/blob/master/Probabilistic/probabilistic.py ) Class file
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### Download (right click and 'save link as'):
@@ -217,10 +218,10 @@ clf.VizPx()
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### Classification and Regression Tree
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#### Requirement: All you need for this is Numpy and matplotlib** (Of course Python >=3.0)
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- ### See the examples in [ Jupyter-Notebook] ( https://github.com /Nikeshbajaj/Machine_Learning_From_Scratch/blob/master/Trees/Example-Classification_and_Regression_V2.0.ipynb ) or [ Repository] ( https://github.com/Nikeshbajaj/Machine_Learning_From_Scratch/tree/master/Trees ) for more details
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+ ### See the examples in [ Jupyter-Notebook] ( https://nbviewer.jupyter.org/github /Nikeshbajaj/Machine_Learning_From_Scratch/blob/master/Trees/Example-Classification_and_Regression_V2.0.ipynb ) or [ Repository] ( https://github.com/Nikeshbajaj/Machine_Learning_From_Scratch/tree/master/Trees ) for more details
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### Notebook
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- [ <img src =" https://raw.githubusercontent.com/Nikeshbajaj/utis/master/images/jp3.png " height =" 50 " />] ( https://github.com /Nikeshbajaj/Machine_Learning_From_Scratch/blob/master/Trees/Example-Classification_and_Regression_V2.0.ipynb )
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+ [ <img src =" https://raw.githubusercontent.com/Nikeshbajaj/utis/master/images/jp3.png " height =" 50 " />] ( https://nbviewer.jupyter.org/github /Nikeshbajaj/Machine_Learning_From_Scratch/blob/master/Trees/Example-Classification_and_Regression_V2.0.ipynb )
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  ;  ;  ;  ;  ;  ;  ;  ;  ;  ; [ <img src =" https://raw.githubusercontent.com/Nikeshbajaj/utis/master/images/jp.png " height =" 50 " />] ( https://github.com/Nikeshbajaj/Machine_Learning_From_Scratch/raw/master/Trees/Example-Classification_and_Regression_V2.0.ipynb )
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### Download (right click and 'save link as'):
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