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How_to_convert_Categorical/Missing_data
How_to_convert_Categorical/Missing_data
 
 
27_Dec_Cat_Dog(1).ipynb
27_Dec_Cat_Dog(1).ipynb
 
 
AI_self_learning_tensor_flow.ipynb
AI_self_learning_tensor_flow.ipynb
 
 
Adult_Census_Income.ipynb
Adult_Census_Income.ipynb
 
 
Categorical_Feature_Encoding_Challenge_how_to_work_with_categorical_data_II.ipynb
Categorical_Feature_Encoding_Challenge_how_to_work_with_categorical_data_II.ipynb
 
 
Clus_K_Means_Customer_Seg_py_v1.ipynb
Clus_K_Means_Customer_Seg_py_v1.ipynb
 
 
Data Science Vs Data Analysis Vs Data Mining Vs Machine Learning.docx
Data Science Vs Data Analysis Vs Data Mining Vs Machine Learning.docx
 
 
Data_cleanning_ways.ipynb
Data_cleanning_ways.ipynb
 
 
Decision_Tree_Practice.ipynb
Decision_Tree_Practice.ipynb
 
 
Deep Learning .ipynb
Deep Learning .ipynb
 
 
Fashion Mnist .ipynb
Fashion Mnist .ipynb
 
 
How_to_Use_Bank_System.ipynb
How_to_Use_Bank_System.ipynb
 
 
How_to_convert_Categorical_Missing_data_data_.ipynb
How_to_convert_Categorical_Missing_data_data_.ipynb
 
 
Intro to Neural Network.ipynb
Intro to Neural Network.ipynb
 
 
Intro_to_Neural_Network.ipynb
Intro_to_Neural_Network.ipynb
 
 
Linear_and_logistic_regression.ipynb
Linear_and_logistic_regression.ipynb
 
 
MNIST_Digit_Data_Set_.ipynb
MNIST_Digit_Data_Set_.ipynb
 
 
Mall_Clustering_.ipynb
Mall_Clustering_.ipynb
 
 
Many_model_&&&_feature_Extraction_from_the_model_.ipynb
Many_model_&&&_feature_Extraction_from_the_model_.ipynb
 
 
Market_Basket_Analyisis_.ipynb
Market_Basket_Analyisis_.ipynb
 
 
Mask_Detector.ipynb
Mask_Detector.ipynb
 
 
Mnist Data Set.ipynb
Mnist Data Set.ipynb
 
 
Multiple_model_Cross_validation_Technique_&_industry_model_devlopement_process_.ipynb
Multiple_model_Cross_validation_Technique_&_industry_model_devlopement_process_.ipynb
 
 
Pandas_profiling_on_Titanic_Dataset.ipynb
Pandas_profiling_on_Titanic_Dataset.ipynb
 
 
Pharma_Covilvelnce_.ipynb
Pharma_Covilvelnce_.ipynb
 
 
Python_Web scrapting.ipynb
Python_Web scrapting.ipynb
 
 
README.md
README.md
 
 
Random_Forest_on_“Cardiotography”_dataset_.ipynb
Random_Forest_on_“Cardiotography”_dataset_.ipynb
 
 
SLC_5CNN_Cat_vs_Dogs_Model_Check_Point_2.ipynb| over come from over fiitting
SLC_5CNN_Cat_vs_Dogs_Model_Check_Point_2.ipynb| over come from over fiitting
 
 
SQLcheat sheet.pdf
SQLcheat sheet.pdf
 
 
Solution_Python_Capstone_Project.ipynb
Solution_Python_Capstone_Project.ipynb
 
 
Some_importent_practice_in_python_Direct_transformer_feature_in_data_science.ipynb
Some_importent_practice_in_python_Direct_transformer_feature_in_data_science.ipynb
 
 
Time_series_model_.ipynb
Time_series_model_.ipynb
 
 
Untitled0.ipynb
Untitled0.ipynb
 
 
abs_all_machine_learning_model_ wok with different catgorical data .ipynb
abs_all_machine_learning_model_ wok with different catgorical data .ipynb
 
 
coloum_selector_chrun_data_.ipynb
coloum_selector_chrun_data_.ipynb
 
 
iris_data_set_Random_Process.ipynb
iris_data_set_Random_Process.ipynb
 
 
machine_learing_point importent point
machine_learing_point importent point
 
 
machine_learing_point_.ipynb
machine_learing_point_.ipynb
 
 

Repository files navigation

Data_science # Machine learning book

https://sebastianraschka.com/resources/ml-lectures-1.html

MACHINE LEARNING BOOK A TO Z https://github.com/abhishekkrthakur/approachingalmost/blob/master/AAAMLP.pdf

PANDA PROFILE VS DATA PREP LIBRARY FOR THE EDA PURPOSE

READ THE ARTICLE https://sfu-db.github.io/dataprep/api_reference/dataprep.html#reference

FOR THE DATA.PREP

https://sfu-db.github.io/dataprep/api_reference/dataprep.html#reference

https://github.com/sfu-db/dataprep

https://sfu-db.github.io/dataprep/user_guide/eda/introduction.html

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data-science machine-learning-algorithms supervised-learning face-recognition missing-data unsupervised-learning visualizations time-series-analysis machinelearning-python

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