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   001 Course Introduction.en.srt

   002 Course Structure.en.srt

   003 Is this Course Right for You_.en.srt

   001 Introducing Data Preparation.en.srt

   002 The Machine Learning Process.en.srt

   003 Data Preparation Defined.en.srt

   004 Choosing a Data Preparation Technique.en.srt

   005 What is Data in Machine Learning_.en.srt

   006 Raw Data.en.srt

   007 Machine Learning is Mostly Data Preparation.en.srt

   008 Common Data Preparation Tasks - Data Cleansing.en.srt

   009 Common Data Preparation Tasks - Feature Selection.en.srt

   010 Common Data Preparation Tasks - Data Transforms.en.srt

   011 Common Data Preparation Tasks - Feature Engineering.en.srt

   012 Common Data Preparation Tasks - Dimensionality Reduction.en.srt

   013 Data Leakage.en.srt

   014 Problem With Naïve Data Preparation.en.srt

   015 Case Study_ Data Leakage_ Train_Test_Split Naïve Approach.en.srt

   016 Case Study_ Data Leakage_ Train_Test_Split Correct Approach.en.srt

   017 Case Study_ Data Leakage_ K-Fold Naïve Approach.en.srt

   018 Case Study_ Data Leakage_ K-Fold Correct Approach.en.srt

   Data Cleansing Master Class - Data Preparation With Training and Testing Sets.ipynb

   001 Data Cleansing Overview.en.srt

   002 Identify Columns That Contain a Single Value.en.srt

   003 Identify Columns with Few Values.en.srt

   004 Remove Columns with Low Variance.en.srt

   005 Identify and Remove Rows That Contain Duplicate Data.en.srt

   006 Defining Outliers.en.srt

   007 Remove Outliers - The Standard Deviation Approach.en.srt

   008 Remove Outliers - The IQR Approach.en.srt

   009 Automatic Outlier Detection.en.srt

   010 Mark Missing Values.en.srt

   011 Remove Rows with Missing Values.en.srt

   012 Statistical Imputation.en.srt

   013 Mean Value Imputation.en.srt

   014 Simple Imputer with Model Evaluation.en.srt

   015 Compare Different Statistical Imputation Strategies.en.srt

   016 K-Nearest Neighbors Imputation.en.srt

   017 KNNImputer and Model Evaluation.en.srt

   018 Iterative Imputation.en.srt

   019 IterativeImputer and Model Evaluation.en.srt

   020 IterativeImputer and Different Imputation Order.en.srt

   030 housing.csv

   Comparing Different Imputed Statistics.ipynb

   IterativeImputer Data Transform.ipynb

   IterativeImputer and Model Evaluation.ipynb

   Mark Missing Values.ipynb

   Outlier Removal - Standard Deviation Approach.ipynb

   SimpleImputer and Model Evaluation.ipynb

   Statistical Imputation With KNN.ipynb

   001 Feature Selection Introduction.en.srt

   002 Feature Selection Defined.en.srt

   003 Statistics for Feature Selection.en.srt

   004 Loading a Categorical Dataset.en.srt

   005 Encode the Dataset for Modeling.en.srt

   006 Chi-Squared.en.srt

   007 Mutual Information.en.srt

   008 Modeling with Selected Categorical Features.en.srt

   009 Feature Selection with ANOVA on Numerical Input.en.srt

   010 Feature Selection with Mutual Information.en.srt

   011 Modeling with Selected Numerical Features.en.srt

   012 Tuning Number of Selected Features.en.srt

   013 Select Features for Numerical Output.en.srt

   014 Linear Correlation with Correlation Statistics.en.srt

   015 Linear Correlation with Mutual Information.en.srt

   016 Baseline and Model Built Using Correlation.en.srt

   017 Model Built Using Mutual Information Features.en.srt

   018 Tuning Number of Selected Features.en.srt

   019 Recursive Feature Elimination.en.srt

   020 RFE for Classification.en.srt

   021 RFE for Regression.en.srt

   022 RFE Hyperparameters.en.srt

   023 Feature Ranking for RFE.en.srt

   024 Feature Importance Scores Defined.en.srt

   025 Feature Importance Scores_ Linear Regression.en.srt

   026 Feature Importance Scores_ Logistic Regression and CART.en.srt

   027 Feature Importance Scores_ Random Forests.en.srt

   028 Permutation Feature Importance.en.srt

   029 Feature Selection with Importance.en.srt

   Categorical Feature Selection.ipynb

   Feature Importance Scores.ipynb

   001 Scale Numerical Data.en.srt

   002 Diabetes Dataset for Scaling.en.srt

   003 MinMaxScaler Transform.en.srt

   004 StandardScaler Transform.en.srt

   005 Robust Scaling Data.en.srt

   006 Robust Scaler Applied to Dataset.en.srt

   007 Explore Robust Scaler Range.en.srt

   008 Nominal and Ordinal Variables.en.srt

   009 Ordinal Encoding.en.srt

   010 One-Hot Encoding Defined.en.srt

   011 One-Hot Encoding.en.srt

   012 Dummy Variable Encoding.en.srt

   013 OrdinalEncoder Transform on Breast Cancer Dataset.en.srt

   014 Make Distributions More Gaussian.en.srt

   015 Power Transform on Contrived Dataset.en.srt

   016 Power Transform on Sonar Dataset.en.srt

   017 Box-Cox on Sonar Dataset.en.srt

   018 Yeo-Johnson on Sonar Dataset.en.srt

   019 Polynomial Features.en.srt

   020 Polynomial Transform on Sonar Dataset.en.srt

   021 Effect of Polynomial Degrees.en.srt

   Data Rescaling .ipynb

   Power Transforms.ipynb

   001 Transforming Different Data Types.en.srt

   002 The ColumnTransformer.en.srt

   003 The ColumnTransformer on Abalone Dataset.en.srt

   004 Manually Transform Target Variable.en.srt

   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

   008 Load and Apply Saved Scalers.en.srt

   094 abalone.csv

   001 Curse of Dimensionality.en.srt

   002 Techniques for Dimensionality Reduction.en.srt

   003 Linear Discriminant Analysis.en.srt

   004 Linear Discriminant Analysis Demonstrated.en.srt

   005 Principal Component Analysis.en.srt

   Dimensionality Reduction.ipynb