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Data Cleansing Master Class in Python
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[TutsNode.com] - Data Cleansing Master Class in Python (Size: 1.41 GB) (Files: 340)
[TutsNode.com] - Data Cleansing Master Class in Python
02 Foundations
007 Machine Learning is Mostly Data Preparation.mp4
006 Raw Data.en.srt
001 Introducing Data Preparation.en.srt
004 Choosing a Data Preparation Technique.en.srt
011 Common Data Preparation Tasks - Feature Engineering.en.srt
012 Common Data Preparation Tasks - Dimensionality Reduction.en.srt
002 The Machine Learning Process.en.srt
013 Data Leakage.en.srt
014 Problem With Naïve Data Preparation.en.srt
016 Case Study_ Data Leakage_ Train_Test_Split Correct Approach.en.srt
018 Data Cleansing Master Class - Data Preparation With Training and Testing Sets.zip
005 What is Data in Machine Learning_.en.srt
001 Introducing Data Preparation.mp4
017 Case Study_ Data Leakage_ K-Fold Naïve Approach.en.srt
007 Machine Learning is Mostly Data Preparation.en.srt
015 Case Study_ Data Leakage_ Train_Test_Split Naïve Approach.en.srt
010 Common Data Preparation Tasks - Data Transforms.en.srt
003 Data Preparation Defined.en.srt
008 Common Data Preparation Tasks - Data Cleansing.en.srt
009 Common Data Preparation Tasks - Feature Selection.en.srt
018 Case Study_ Data Leakage_ K-Fold Correct Approach.en.srt
003 Data Preparation Defined.mp4
004 Choosing a Data Preparation Technique.mp4
014 Problem With Naïve Data Preparation.mp4
008 Common Data Preparation Tasks - Data Cleansing.mp4
011 Common Data Preparation Tasks - Feature Engineering.mp4
006 Raw Data.mp4
005 What is Data in Machine Learning_.mp4
015 Case Study_ Data Leakage_ Train_Test_Split Naïve Approach.mp4
017 Case Study_ Data Leakage_ K-Fold Naïve Approach.mp4
002 The Machine Learning Process.mp4
018 Case Study_ Data Leakage_ K-Fold Correct Approach.mp4
016 Case Study_ Data Leakage_ Train_Test_Split Correct Approach.mp4
013 Data Leakage.mp4
009 Common Data Preparation Tasks - Feature Selection.mp4
010 Common Data Preparation Tasks - Data Transforms.mp4
012 Common Data Preparation Tasks - Dimensionality Reduction.mp4
01 Introduction
001 Course Introduction.en.srt
003 Is this Course Right for You_.en.srt
002 Course Structure.en.srt
002 Course Structure.mp4
001 Course Introduction.mp4
003 Is this Course Right for You_.mp4
05 Data Transforms
085 Power Transforms.zip
072 Data Rescaling .zip
089 Polynomial Feature Transform.zip
005 Robust Scaling Data.en.srt
019 Polynomial Features.en.srt
020 Polynomial Transform on Sonar Dataset.en.srt
013 OrdinalEncoder Transform on Breast Cancer Dataset.en.srt
008 Nominal and Ordinal Variables.en.srt
015 Power Transform on Contrived Dataset.en.srt
009 Ordinal Encoding.en.srt
012 Dummy Variable Encoding.en.srt
017 Box-Cox on Sonar Dataset.en.srt
014 Make Distributions More Gaussian.en.srt
011 One-Hot Encoding.en.srt
016 Power Transform on Sonar Dataset.en.srt
001 Scale Numerical Data.en.srt
021 Effect of Polynomial Degrees.en.srt
018 Yeo-Johnson on Sonar Dataset.en.srt
004 StandardScaler Transform.en.srt
002 Diabetes Dataset for Scaling.en.srt
003 MinMaxScaler Transform.en.srt
006 Robust Scaler Applied to Dataset.en.srt
007 Explore Robust Scaler Range.en.srt
010 One-Hot Encoding Defined.en.srt
008 Nominal and Ordinal Variables.mp4
019 Polynomial Features.mp4
020 Polynomial Transform on Sonar Dataset.mp4
013 OrdinalEncoder Transform on Breast Cancer Dataset.mp4
005 Robust Scaling Data.mp4
017 Box-Cox on Sonar Dataset.mp4
016 Power Transform on Sonar Dataset.mp4
004 StandardScaler Transform.mp4
018 Yeo-Johnson on Sonar Dataset.mp4
003 MinMaxScaler Transform.mp4
002 Diabetes Dataset for Scaling.mp4
015 Power Transform on Contrived Dataset.mp4
006 Robust Scaler Applied to Dataset.mp4
021 Effect of Polynomial Degrees.mp4
009 Ordinal Encoding.mp4
012 Dummy Variable Encoding.mp4
011 One-Hot Encoding.mp4
007 Explore Robust Scaler Range.mp4
001 Scale Numerical Data.mp4
014 Make Distributions More Gaussian.mp4
010 One-Hot Encoding Defined.mp4
03 Data Cleansing
030 housing.csv
023 Sparse Column Identification and Removal.zip
041 IterativeImputer and Different Number of Iterations.zip
038 KNNImputer and Model Evaluation Different K-Values.zip
036 Comparing Different Imputed Statistics.zip
010 Mark Missing Values.en.srt
007 Remove Outliers - The Standard Deviation Approach.en.srt
009 Automatic Outlier Detection.en.srt
016 K-Nearest Neighbors Imputation.en.srt
013 Mean Value Imputation.en.srt
001 Data Cleansing Overview.en.srt
003 Identify Columns with Few Values.en.srt
006 Defining Outliers.en.srt
034 Statistical Imputation With SimpleImputer.zip
018 Iterative Imputation.en.srt
005 Identify and Remove Rows That Contain Duplicate Data.en.srt
011 Remove Rows with Missing Values.en.srt
012 Statistical Imputation.en.srt
014 Simple Imputer with Model Evaluation.en.srt
004 Remove Columns with Low Variance.en.srt
015 Compare Different Statistical Imputation Strategies.en.srt
008 Remove Outliers - The IQR Approach.en.srt
019 IterativeImputer and Model Evaluation.en.srt
020 IterativeImputer and Different Imputation Order.en.srt
026 Identify and Remove Duplicate Rows.zip
028 Outlier Removal - Standard Deviation Approach.zip
029 Outlier Removal - IQR Approach.zip
030 Automatic Outlier Detection.zip
031 Mark Missing Values.zip
032 Remove Missing Values.zip
035 SimpleImputer and Model Evaluation.zip
017 KNNImputer and Model Evaluation.en.srt
037 Statistical Imputation With KNN.zip
039 IterativeImputer Data Transform.zip
040 IterativeImputer and Model Evaluation.zip
002 Identify Columns That Contain a Single Value.en.srt
010 Mark Missing Values.mp4
001 Data Cleansing Overview.mp4
009 Automatic Outlier Detection.mp4
007 Remove Outliers - The Standard Deviation Approach.mp4
016 K-Nearest Neighbors Imputation.mp4
013 Mean Value Imputation.mp4
005 Identify and Remove Rows That Contain Duplicate Data.mp4
008 Remove Outliers - The IQR Approach.mp4
006 Defining Outliers.mp4
018 Iterative Imputation.mp4
017 KNNImputer and Model Evaluation.mp4
003 Identify Columns with Few Values.mp4
004 Remove Columns with Low Variance.mp4
011 Remove Rows with Missing Values.mp4
015 Compare Different Statistical Imputation Strategies.mp4
020 IterativeImputer and Different Imputation Order.mp4
014 Simple Imputer with Model Evaluation.mp4
002 Identify Columns That Contain a Single Value.mp4
019 IterativeImputer and Model Evaluation.mp4
012 Statistical Imputation.mp4
06 Advanced Transforms
094 abalone.csv
092 Advanced Transforms.zip
005 Automatically Transform Target Variable.en.srt
006 Challenge of Preparing New Data for a Model.en.srt
007 Save Model and Data Scaler.en.srt
006 Challenge of Preparing New Data for a Model.mp4
003 The ColumnTransformer on Abalone Dataset.en.srt
004 Manually Transform Target Variable.en.srt
001 Transforming Different Data Types.en.srt
002 The ColumnTransformer.en.srt
008 Load and Apply Saved Scalers.en.srt
005 Automatically Transform Target Variable.mp4
007 Save Model and Data Scaler.mp4
004 Manually Transform Target Variable.mp4
003 The ColumnTransformer on Abalone Dataset.mp4
002 The ColumnTransformer.mp4
001 Transforming Different Data Types.mp4
008 Load and Apply Saved Scalers.mp4
04 Feature Selection
066 Feature Importance Scores.zip
054 Select Features for Numerical Output.zip
050 Choosing Numerical Input Features.zip
045 Categorical Feature Selection.zip
009 Feature Selection with ANOVA on Numerical Input.en.srt
018 Tuning Number of Selected Features.en.srt
020 RFE for Classification.en.srt
026 Feature Importance Scores_ Logistic Regression and CART.en.srt
029 Feature Selection with Importance.en.srt
002 Feature Selection Defined.en.srt
025 Feature Importance Scores_ Linear Regression.en.srt
008 Modeling with Selected Categorical Features.en.srt
024 Feature Importance Scores Defined.en.srt
012 Tuning Number of Selected Features.en.srt
019 Recursive Feature Elimination.en.srt
022 RFE Hyperparameters.en.srt
013 Select Features for Numerical Output.en.srt
004 Loading a Categorical Dataset.en.srt
001 Feature Selection Introduction.en.srt
014 Linear Correlation with Correlation Statistics.en.srt
003 Statistics for Feature Selection.en.srt
028 Permutation Feature Importance.en.srt
005 Encode the Dataset for Modeling.en.srt
006 Chi-Squared.en.srt
007 Mutual Information.en.srt
015 Linear Correlation with Mutual Information.en.srt
016 Baseline and Model Built Using Correlation.en.srt
023 Feature Ranking for RFE.en.srt
010 Feature Selection with Mutual Information.en.srt
011 Modeling with Selected Numerical Features.en.srt
021 RFE for Regression.en.srt
017 Model Built Using Mutual Information Features.en.srt
027 Feature Importance Scores_ Random Forests.en.srt
019 Recursive Feature Elimination.mp4
024 Feature Importance Scores Defined.mp4
018 Tuning Number of Selected Features.mp4
001 Feature Selection Introduction.mp4
020 RFE for Classification.mp4
009 Feature Selection with ANOVA on Numerical Input.mp4
029 Feature Selection with Importance.mp4
012 Tuning Number of Selected Features.mp4
026 Feature Importance Scores_ Logistic Regression and CART.mp4
008 Modeling with Selected Categorical Features.mp4
025 Feature Importance Scores_ Linear Regression.mp4
016 Baseline and Model Built Using Correlation.mp4
022 RFE Hyperparameters.mp4
023 Feature Ranking for RFE.mp4
015 Linear Correlation with Mutual Information.mp4
028 Permutation Feature Importance.mp4
004 Loading a Categorical Dataset.mp4
014 Linear Correlation with Correlation Statistics.mp4
011 Modeling with Selected Numerical Features.mp4
003 Statistics for Feature Selection.mp4
005 Encode the Dataset for Modeling.mp4
021 RFE for Regression.mp4
013 Select Features for Numerical Output.mp4
010 Feature Selection with Mutual Information.mp4
006 Chi-Squared.mp4
007 Mutual Information.mp4
027 Feature Importance Scores_ Random Forests.mp4
002 Feature Selection Defined.mp4
017 Model Built Using Mutual Information Features.mp4
07 Dimensionality Reduction
100 Dimensionality Reduction.zip
005 Principal Component Analysis.en.srt
004 Linear Discriminant Analysis Demonstrated.en.srt
002 Techniques for Dimensionality Reduction.en.srt
003 Linear Discriminant Analysis.en.srt
001 Curse of Dimensionality.en.srt
005 Principal Component Analysis.mp4
004 Linear Discriminant Analysis Demonstrated.mp4
002 Techniques for Dimensionality Reduction.mp4
003 Linear Discriminant Analysis.mp4
001 Curse of Dimensionality.mp4
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