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Building_your_Deep_Neural_Network_Step_by_Step_v8a.ipynb
Building_your_Deep_Neural_Network_Step_by_Step_v8a.ipynb
 
 
Convolution_model_Application_v1a.ipynb
Convolution_model_Application_v1a.ipynb
 
 
Convolution_model_Step_by_Step_v2a.ipynb
Convolution_model_Step_by_Step_v2a.ipynb
 
 
Deep+Neural+Network+-+Application+v8.ipynb
Deep+Neural+Network+-+Application+v8.ipynb
 
 
Gradient+Checking+v1.ipynb
Gradient+Checking+v1.ipynb
 
 
HyperTuning.ipynb
HyperTuning.ipynb
 
 
Initialization.ipynb
Initialization.ipynb
 
 
Optimization_methods_v1b.ipynb
Optimization_methods_v1b.ipynb
 
 
P1.ipynb
P1.ipynb
 
 
Planar_data_classification_with_onehidden_layer_v6c.ipynb
Planar_data_classification_with_onehidden_layer_v6c.ipynb
 
 
Python_Basics_With_Numpy_v3a.ipynb
Python_Basics_With_Numpy_v3a.ipynb
 
 
README.md
README.md
 
 
Random Forest.ipynb
Random Forest.ipynb
 
 
Regularization_v2a.ipynb
Regularization_v2a.ipynb
 
 
SCIKITLinearRegression.ipynb
SCIKITLinearRegression.ipynb
 
 
SCIKITLogisticsRegression.ipynb
SCIKITLogisticsRegression.ipynb
 
 
SVM.ipynb
SVM.ipynb
 
 
intro _to_neural_nets_tensorflow.ipynb
intro _to_neural_nets_tensorflow.ipynb
 
 
tensorflow(linearregression).ipynb
tensorflow(linearregression).ipynb
 
 
tensorflow(logisticregression).ipynb
tensorflow(logisticregression).ipynb
 
 

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ML_python

basic ml_python codes This has basic ML codes namely: 1.Linear and Logistic Regression 2.SVM 3.Random Forests 4.Scikit Implementation of neural network using grid searchcv and random searchcv

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