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.ipynb_checkpoints
.ipynb_checkpoints
 
 
10_Predicting_Continuous_Target_Variables_With_Regression_Analysis
10_Predicting_Continuous_Target_Variables_With_Regression_Analysis
 
 
11_Working_With_Unlabeled_Data_Clustering_Analysis
11_Working_With_Unlabeled_Data_Clustering_Analysis
 
 
12_Implementing_A_Multilayer_Artificial_Neural_Network_From_Scratch
12_Implementing_A_Multilayer_Artificial_Neural_Network_From_Scratch
 
 
13_Parallelizing_Neural_Network_Training_With_TensorFlow
13_Parallelizing_Neural_Network_Training_With_TensorFlow
 
 
14_Going_Deeper_The_Mechanics_Of_TensorFlow
14_Going_Deeper_The_Mechanics_Of_TensorFlow
 
 
15_Classifying_Images_With_Deep_Convolutional_Neural_Networks
15_Classifying_Images_With_Deep_Convolutional_Neural_Networks
 
 
16_Modeling_Sequential_Data_Using_Recurrent_Neural_Networks
16_Modeling_Sequential_Data_Using_Recurrent_Neural_Networks
 
 
1_Giving_Computers_The_Ability_To_Learn_From_Data
1_Giving_Computers_The_Ability_To_Learn_From_Data
 
 
2_Training_Simple_Machine_Learning_Algorithms_For_Classification
2_Training_Simple_Machine_Learning_Algorithms_For_Classification
 
 
3_A_Tour_Of_Machine_Learning_Classifiers_Using_Scikit_Learn
3_A_Tour_Of_Machine_Learning_Classifiers_Using_Scikit_Learn
 
 
4_Building_Good_Training_Sets_Data_Preprocessing
4_Building_Good_Training_Sets_Data_Preprocessing
 
 
5_Compressing_Data_Via_Dimensionality_Reduction
5_Compressing_Data_Via_Dimensionality_Reduction
 
 
6_Learning_Best_Practices_For_Model_Evaluation_And_Hyperparameter_Tuning
6_Learning_Best_Practices_For_Model_Evaluation_And_Hyperparameter_Tuning
 
 
7_Combining_Different_Models_For_Ensemble_Learning
7_Combining_Different_Models_For_Ensemble_Learning
 
 
8_Applying_Machine_Learning_To_Sentiment_Analysis
8_Applying_Machine_Learning_To_Sentiment_Analysis
 
 
9_Embedding_A_Machine_Learning_Model_Into_A_Web_Application
9_Embedding_A_Machine_Learning_Model_Into_A_Web_Application
 
 
README
README
 
 

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Reproduction and summary of the contents of the book Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition, by Sebastian Raschka and Vahid Mirjalili

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