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102.4 B
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001 Course Introduction.en.srt
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2.8 KB
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001 Course Introduction.mp4
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MP4
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22.6 MB
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001 Curse of Dimensionality.en.srt
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2.7 KB
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001 Curse of Dimensionality.mp4
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6.2 MB
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001 Data Cleansing Overview.en.srt
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2.2 KB
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001 Data Cleansing Overview.mp4
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20 MB
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001 Feature Selection Introduction.en.srt
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2.4 KB
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1
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512 B
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001 Feature Selection Introduction.mp4
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19.5 MB
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001 Introducing Data Preparation.en.srt
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2.8 KB
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001 Introducing Data Preparation.mp4
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MP4
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36.4 MB
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001 Scale Numerical Data.en.srt
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2.7 KB
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001 Scale Numerical Data.mp4
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5.1 MB
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001 Transforming Different Data Types.en.srt
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3.1 KB
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001 Transforming Different Data Types.mp4
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8.9 MB
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002 Course Structure.en.srt
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3.6 KB
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002 Course Structure.mp4
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23.9 MB
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002 Feature Selection Defined.en.srt
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4.4 KB
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002 Feature Selection Defined.mp4
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5.2 MB
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002 The ColumnTransformer.mp4
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10.5 MB
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002 The Machine Learning Process.mp4
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14.3 MB
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2
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281.9 KB
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002 Diabetes Dataset for Scaling.en.srt
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2.5 KB
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002 Diabetes Dataset for Scaling.mp4
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8.7 MB
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002 Identify Columns That Contain a Single Value.en.srt
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3.2 KB
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002 Identify Columns That Contain a Single Value.mp4
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7.5 MB
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002 Techniques for Dimensionality Reduction.en.srt
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4.9 KB
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002 Techniques for Dimensionality Reduction.mp4
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13 MB
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002 The ColumnTransformer.en.srt
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3.1 KB
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002 The Machine Learning Process.en.srt
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5.4 KB
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003 Data Preparation Defined.mp4
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30.2 MB
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3
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280.8 KB
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003 Data Preparation Defined.en.srt
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3.8 KB
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003 Identify Columns with Few Values.en.srt
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4.2 KB
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003 Identify Columns with Few Values.mp4
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12 MB
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003 Is this Course Right for You_.en.srt
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1.7 KB
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003 Is this Course Right for You_.mp4
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1.6 MB
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003 Linear Discriminant Analysis.en.srt
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3 KB
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003 Linear Discriminant Analysis.mp4
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7.6 MB
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003 MinMaxScaler Transform.en.srt
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2.3 KB
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003 MinMaxScaler Transform.mp4
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8.9 MB
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003 Statistics for Feature Selection.en.srt
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3 KB
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003 Statistics for Feature Selection.mp4
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9.5 MB
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003 The ColumnTransformer on Abalone Dataset.en.srt
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3.7 KB
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003 The ColumnTransformer on Abalone Dataset.mp4
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13.1 MB
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004 Choosing a Data Preparation Technique.en.srt
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2.7 KB
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004 Choosing a Data Preparation Technique.mp4
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MP4
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25.9 MB
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004 Linear Discriminant Analysis Demonstrated.en.srt
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5.3 KB
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004 Linear Discriminant Analysis Demonstrated.mp4
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18.6 MB
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004 Loading a Categorical Dataset.en.srt
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3.4 KB
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004 Loading a Categorical Dataset.mp4
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10.3 MB
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004 Manually Transform Target Variable.en.srt
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3.4 KB
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004 Manually Transform Target Variable.mp4
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13.2 MB
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004 Remove Columns with Low Variance.en.srt
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3.8 KB
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004 Remove Columns with Low Variance.mp4
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11.2 MB
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004 StandardScaler Transform.en.srt
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2.6 KB
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004 StandardScaler Transform.mp4
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10.5 MB
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005 Automatically Transform Target Variable.mp4
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20.4 MB
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005 Automatically Transform Target Variable.en.srt
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005 Encode the Dataset for Modeling.en.srt
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3.1 KB
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005 Encode the Dataset for Modeling.mp4
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9.4 MB
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005 Identify and Remove Rows That Contain Duplicate Data.en.srt
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3.9 KB
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005 Identify and Remove Rows That Contain Duplicate Data.mp4
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15.6 MB
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005 Principal Component Analysis.en.srt
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7.2 KB
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005 Principal Component Analysis.mp4
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MP4
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22.6 MB
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005 Robust Scaling Data.en.srt
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005 Robust Scaling Data.mp4
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16.5 MB
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005 What is Data in Machine Learning_.en.srt
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4.7 KB
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005 What is Data in Machine Learning_.mp4
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17.9 MB
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006 Challenge of Preparing New Data for a Model.en.srt
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006 Challenge of Preparing New Data for a Model.mp4
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34.1 MB
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006 Chi-Squared.en.srt
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3 KB
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006 Chi-Squared.mp4
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7 MB
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006 Defining Outliers.en.srt
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2.7 KB
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006 Defining Outliers.mp4
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14.4 MB
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006 Raw Data.en.srt
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006 Raw Data.mp4
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20.5 MB
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006 Robust Scaler Applied to Dataset.en.srt
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2.2 KB
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006 Robust Scaler Applied to Dataset.mp4
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8.4 MB
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007 Explore Robust Scaler Range.en.srt
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1.6 KB
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007 Explore Robust Scaler Range.mp4
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5.6 MB
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007 Machine Learning is Mostly Data Preparation.en.srt
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4.1 KB
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007 Machine Learning is Mostly Data Preparation.mp4
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40.9 MB
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007 Mutual Information.en.srt
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2.2 KB
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007 Mutual Information.mp4
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6.9 MB
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007 Remove Outliers - The Standard Deviation Approach.en.srt
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007 Remove Outliers - The Standard Deviation Approach.mp4
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18.5 MB
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007 Save Model and Data Scaler.en.srt
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3.8 KB
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007 Save Model and Data Scaler.mp4
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15.2 MB
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008 Common Data Preparation Tasks - Data Cleansing.en.srt
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3.7 KB
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008 Load and Apply Saved Scalers.en.srt
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2 KB
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008 Common Data Preparation Tasks - Data Cleansing.mp4
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21.7 MB
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008 Load and Apply Saved Scalers.mp4
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6.6 MB
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008 Modeling with Selected Categorical Features.en.srt
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4 KB
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008 Modeling with Selected Categorical Features.mp4
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14.1 MB
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008 Nominal and Ordinal Variables.en.srt
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4.4 KB
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008 Nominal and Ordinal Variables.mp4
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26 MB
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008 Remove Outliers - The IQR Approach.en.srt
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3.8 KB
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008 Remove Outliers - The IQR Approach.mp4
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14.9 MB
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009 Common Data Preparation Tasks - Feature Selection.mp4
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7.9 MB
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152.9 KB
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009 Automatic Outlier Detection.en.srt
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5.2 KB
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009 Automatic Outlier Detection.mp4
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18.6 MB
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009 Common Data Preparation Tasks - Feature Selection.en.srt
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3.5 KB
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009 Feature Selection with ANOVA on Numerical Input.en.srt
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6.4 KB
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009 Feature Selection with ANOVA on Numerical Input.mp4
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MP4
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17.2 MB
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009 Ordinal Encoding.en.srt
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3.3 KB
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009 Ordinal Encoding.mp4
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7 MB
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10
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010 Common Data Preparation Tasks - Data Transforms.en.srt
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3.9 KB
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010 Common Data Preparation Tasks - Data Transforms.mp4
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4.7 MB
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010 Feature Selection with Mutual Information.en.srt
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2.7 KB
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010 Feature Selection with Mutual Information.mp4
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7.3 MB
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010 Mark Missing Values.en.srt
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6.8 KB
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010 Mark Missing Values.mp4
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MP4
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22.7 MB
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010 One-Hot Encoding Defined.en.srt
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1.3 KB
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010 One-Hot Encoding Defined.mp4
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1.7 MB
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011 Common Data Preparation Tasks - Feature Engineering.en.srt
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2.2 KB
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011 Common Data Preparation Tasks - Feature Engineering.mp4
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MP4
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21.6 MB
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011 Modeling with Selected Numerical Features.en.srt
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2.6 KB
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011 Modeling with Selected Numerical Features.mp4
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MP4
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9.7 MB
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011 One-Hot Encoding.en.srt
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2.9 KB
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011 One-Hot Encoding.mp4
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6.8 MB
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011 Remove Rows with Missing Values.en.srt
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2.4 KB
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11
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011 Remove Rows with Missing Values.mp4
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10 MB
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012 Common Data Preparation Tasks - Dimensionality Reduction.en.srt
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2.9 KB
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012 Common Data Preparation Tasks - Dimensionality Reduction.mp4
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MP4
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4.1 MB
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012 Dummy Variable Encoding.en.srt
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3.1 KB
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012 Dummy Variable Encoding.mp4
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7 MB
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012 Statistical Imputation.en.srt
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2 KB
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12
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012 Statistical Imputation.mp4
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2.6 MB
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012 Tuning Number of Selected Features.en.srt
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012 Tuning Number of Selected Features.mp4
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14.4 MB
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013 Data Leakage.en.srt
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1.1 KB
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013 Data Leakage.mp4
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8.8 MB
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013 Mean Value Imputation.en.srt
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4.9 KB
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013 Mean Value Imputation.mp4
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15.9 MB
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013 OrdinalEncoder Transform on Breast Cancer Dataset.en.srt
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5 KB
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013 OrdinalEncoder Transform on Breast Cancer Dataset.mp4
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17.1 MB
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013 Select Features for Numerical Output.en.srt
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3.4 KB
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013 Select Features for Numerical Output.mp4
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8.7 MB
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13
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014 Linear Correlation with Correlation Statistics.en.srt
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3.3 KB
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014 Linear Correlation with Correlation Statistics.mp4
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9.9 MB
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014 Make Distributions More Gaussian.en.srt
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2.9 KB
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014 Make Distributions More Gaussian.mp4
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4 MB
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014 Problem With Naïve Data Preparation.en.srt
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5.2 KB
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014 Problem With Naïve Data Preparation.mp4
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24.9 MB
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014 Simple Imputer with Model Evaluation.en.srt
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1.8 KB
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014 Simple Imputer with Model Evaluation.mp4
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7.6 MB
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14
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015 Case Study_ Data Leakage_ Train_Test_Split Naïve Approach.en.srt
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3.9 KB
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015 Case Study_ Data Leakage_ Train_Test_Split Naïve Approach.mp4
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MP4
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16.5 MB
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015 Compare Different Statistical Imputation Strategies.en.srt
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2.5 KB
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015 Compare Different Statistical Imputation Strategies.mp4
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MP4
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9.3 MB
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015 Linear Correlation with Mutual Information.en.srt
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3.1 KB
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015 Linear Correlation with Mutual Information.mp4
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10.8 MB
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015 Power Transform on Contrived Dataset.en.srt
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3.6 KB
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15
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015 Power Transform on Contrived Dataset.mp4
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8.5 MB
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016 Baseline and Model Built Using Correlation.en.srt
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3.1 KB
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016 Baseline and Model Built Using Correlation.mp4
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13.1 MB
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016 Case Study_ Data Leakage_ Train_Test_Split Correct Approach.en.srt
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2.4 KB
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016 Case Study_ Data Leakage_ Train_Test_Split Correct Approach.mp4
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9.5 MB
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16
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016 K-Nearest Neighbors Imputation.en.srt
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5.1 KB
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016 K-Nearest Neighbors Imputation.mp4
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16.9 MB
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016 Power Transform on Sonar Dataset.en.srt
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2.9 KB
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016 Power Transform on Sonar Dataset.mp4
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10.9 MB
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017 Box-Cox on Sonar Dataset.en.srt
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3.1 KB
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017 Box-Cox on Sonar Dataset.mp4
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11.7 MB
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017 Case Study_ Data Leakage_ K-Fold Naïve Approach.en.srt
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4.2 KB
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017 Case Study_ Data Leakage_ K-Fold Naïve Approach.mp4
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MP4
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14.3 MB
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017 KNNImputer and Model Evaluation.en.srt
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3.4 KB
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017 KNNImputer and Model Evaluation.mp4
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12.9 MB
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017 Model Built Using Mutual Information Features.en.srt
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1 KB
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017 Model Built Using Mutual Information Features.mp4
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3.9 MB
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17
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018 Case Study_ Data Leakage_ K-Fold Correct Approach.en.srt
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3.1 KB
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018 Case Study_ Data Leakage_ K-Fold Correct Approach.mp4
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12.8 MB
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018 Data Cleansing Master Class - Data Preparation With Training and Testing Sets.zip
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ZIP
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1.5 KB
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018 Iterative Imputation.en.srt
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4.1 KB
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018 Iterative Imputation.mp4
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MP4
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13.8 MB
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018 Tuning Number of Selected Features.en.srt
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4.8 KB
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018 Tuning Number of Selected Features.mp4
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20.3 MB
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018 Yeo-Johnson on Sonar Dataset.en.srt
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2.7 KB
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018 Yeo-Johnson on Sonar Dataset.mp4
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9.6 MB
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019 IterativeImputer and Model Evaluation.mp4
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MP4
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6.5 MB
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019 Polynomial Features.mp4
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20.7 MB
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019 Recursive Feature Elimination.mp4
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27.9 MB
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19
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170 KB
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019 IterativeImputer and Model Evaluation.en.srt
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1.4 KB
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019 Polynomial Features.en.srt
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5.2 KB
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019 Recursive Feature Elimination.en.srt
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3.8 KB
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020 IterativeImputer and Different Imputation Order.en.srt
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2.2 KB
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020 IterativeImputer and Different Imputation Order.mp4
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MP4
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8.4 MB
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020 Polynomial Transform on Sonar Dataset.mp4
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MP4
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20.6 MB
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020 RFE for Classification.en.srt
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4.6 KB
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20
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020 Polynomial Transform on Sonar Dataset.en.srt
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5.2 KB
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020 RFE for Classification.mp4
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MP4
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18.5 MB
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021 Effect of Polynomial Degrees.en.srt
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2.7 KB
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021 Effect of Polynomial Degrees.mp4
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7.5 MB
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021 RFE for Regression.en.srt
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2.6 KB
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021 RFE for Regression.mp4
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9.4 MB
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21
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022 RFE Hyperparameters.mp4
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12.1 MB
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22
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022 RFE Hyperparameters.en.srt
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3.4 KB
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023 Feature Ranking for RFE.en.srt
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3 KB
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023 Feature Ranking for RFE.mp4
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MP4
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10.9 MB
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023 Sparse Column Identification and Removal.zip
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ZIP
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10.1 KB
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23
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404.8 KB
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024 Feature Importance Scores Defined.en.srt
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SRT
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024 Feature Importance Scores Defined.mp4
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025 Feature Importance Scores_ Linear Regression.en.srt
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025 Feature Importance Scores_ Linear Regression.mp4
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026 Feature Importance Scores_ Logistic Regression and CART.en.srt
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026 Feature Importance Scores_ Logistic Regression and CART.mp4
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026 Identify and Remove Duplicate Rows.zip
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027 Feature Importance Scores_ Random Forests.en.srt
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027 Feature Importance Scores_ Random Forests.mp4
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028 Outlier Removal - Standard Deviation Approach.zip
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028 Permutation Feature Importance.en.srt
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028 Permutation Feature Importance.mp4
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029 Feature Selection with Importance.en.srt
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029 Feature Selection with Importance.mp4
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029 Outlier Removal - IQR Approach.zip
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030 Automatic Outlier Detection.zip
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030 housing.csv
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031 Mark Missing Values.zip
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032 Remove Missing Values.zip
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034 Statistical Imputation With SimpleImputer.zip
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035 SimpleImputer and Model Evaluation.zip
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036 Comparing Different Imputed Statistics.zip
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037 Statistical Imputation With KNN.zip
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038 KNNImputer and Model Evaluation Different K-Values.zip
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8 KB
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039 IterativeImputer Data Transform.zip
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040 IterativeImputer and Model Evaluation.zip
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041 IterativeImputer and Different Number of Iterations.zip
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045 Categorical Feature Selection.zip
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050 Choosing Numerical Input Features.zip
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TutsNode.com.txt
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[TGx]Downloaded from torrentgalaxy.to .txt
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054 Select Features for Numerical Output.zip
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066 Feature Importance Scores.zip
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072 Data Rescaling .zip
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085 Power Transforms.zip
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089 Polynomial Feature Transform.zip
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092 Advanced Transforms.zip
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094 abalone.csv
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100 Dimensionality Reduction.zip
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100
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101
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