| 001 Course Introduction.en.srt | 2.8 KB | ||
| 001 Course Introduction.mp4 | 22.6 MB | ||
| 001 Curse of Dimensionality.en.srt | 2.7 KB | ||
| 001 Curse of Dimensionality.mp4 | 6.2 MB | ||
| 001 Data Cleansing Overview.en.srt | 2.2 KB | ||
| 001 Data Cleansing Overview.mp4 | 20 MB | ||
| 001 Feature Selection Introduction.en.srt | 2.4 KB | ||
| 001 Feature Selection Introduction.mp4 | 19.5 MB | ||
| 001 Introducing Data Preparation.en.srt | 2.8 KB | ||
| 001 Introducing Data Preparation.mp4 | 36.4 MB | ||
| 001 Scale Numerical Data.en.srt | 2.7 KB | ||
| 001 Scale Numerical Data.mp4 | 5.1 MB | ||
| 001 Transforming Different Data Types.en.srt | 3.1 KB | ||
| 001 Transforming Different Data Types.mp4 | 8.9 MB | ||
| 002 Course Structure.en.srt | 3.6 KB | ||
| 002 Course Structure.mp4 | 23.9 MB | ||
| 002 Diabetes Dataset for Scaling.en.srt | 2.5 KB | ||
| 002 Diabetes Dataset for Scaling.mp4 | 8.7 MB | ||
| 002 Feature Selection Defined.en.srt | 4.4 KB | ||
| 002 Feature Selection Defined.mp4 | 5.2 MB | ||
| 002 Identify Columns That Contain a Single Value.en.srt | 3.2 KB | ||
| 002 Identify Columns That Contain a Single Value.mp4 | 7.5 MB | ||
| 002 Techniques for Dimensionality Reduction.en.srt | 4.9 KB | ||
| 002 Techniques for Dimensionality Reduction.mp4 | 13 MB | ||
| 002 The ColumnTransformer.en.srt | 3.1 KB | ||
| 002 The ColumnTransformer.mp4 | 10.5 MB | ||
| 002 The Machine Learning Process.en.srt | 5.4 KB | ||
| 002 The Machine Learning Process.mp4 | 14.3 MB | ||
| 003 Data Preparation Defined.en.srt | 3.8 KB | ||
| 003 Data Preparation Defined.mp4 | 30.2 MB | ||
| 003 Identify Columns with Few Values.en.srt | 4.2 KB | ||
| 003 Identify Columns with Few Values.mp4 | 12 MB | ||
| 003 Is this Course Right for You_.en.srt | 1.7 KB | ||
| 003 Is this Course Right for You_.mp4 | 1.6 MB | ||
| 003 Linear Discriminant Analysis.en.srt | 3 KB | ||
| 003 Linear Discriminant Analysis.mp4 | 7.6 MB | ||
| 003 MinMaxScaler Transform.en.srt | 2.3 KB | ||
| 003 MinMaxScaler Transform.mp4 | 8.9 MB | ||
| 003 Statistics for Feature Selection.en.srt | 3 KB | ||
| 003 Statistics for Feature Selection.mp4 | 9.5 MB | ||
| 003 The ColumnTransformer on Abalone Dataset.en.srt | 3.7 KB | ||
| 003 The ColumnTransformer on Abalone Dataset.mp4 | 13.1 MB | ||
| 004 Choosing a Data Preparation Technique.en.srt | 2.7 KB | ||
| 004 Choosing a Data Preparation Technique.mp4 | 25.9 MB | ||
| 004 Linear Discriminant Analysis Demonstrated.en.srt | 5.3 KB | ||
| 004 Linear Discriminant Analysis Demonstrated.mp4 | 18.6 MB | ||
| 004 Loading a Categorical Dataset.en.srt | 3.4 KB | ||
| 004 Loading a Categorical Dataset.mp4 | 10.3 MB | ||
| 004 Manually Transform Target Variable.en.srt | 3.4 KB | ||
| 004 Manually Transform Target Variable.mp4 | 13.2 MB | ||
| 004 Remove Columns with Low Variance.en.srt | 3.8 KB | ||
| 004 Remove Columns with Low Variance.mp4 | 11.2 MB | ||
| 004 StandardScaler Transform.en.srt | 2.6 KB | ||
| 004 StandardScaler Transform.mp4 | 10.5 MB | ||
| 005 Automatically Transform Target Variable.en.srt | 5.4 KB | ||
| 005 Automatically Transform Target Variable.mp4 | 20.4 MB | ||
| 005 Encode the Dataset for Modeling.en.srt | 3.1 KB | ||
| 005 Encode the Dataset for Modeling.mp4 | 9.4 MB | ||
| 005 Identify and Remove Rows That Contain Duplicate Data.en.srt | 3.9 KB | ||
| 005 Identify and Remove Rows That Contain Duplicate Data.mp4 | 15.6 MB | ||
| 005 Principal Component Analysis.en.srt | 7.2 KB | ||
| 005 Principal Component Analysis.mp4 | 22.6 MB | ||
| 005 Robust Scaling Data.en.srt | 5.6 KB | ||
| 005 Robust Scaling Data.mp4 | 16.5 MB | ||
| 005 What is Data in Machine Learning_.en.srt | 4.7 KB | ||
| 005 What is Data in Machine Learning_.mp4 | 17.9 MB | ||
| 006 Challenge of Preparing New Data for a Model.en.srt | 4.9 KB | ||
| 006 Challenge of Preparing New Data for a Model.mp4 | 34.1 MB | ||
| 006 Chi-Squared.en.srt | 3 KB | ||
| 006 Chi-Squared.mp4 | 7 MB | ||
| 006 Defining Outliers.en.srt | 2.7 KB | ||
| 006 Defining Outliers.mp4 | 14.4 MB | ||
| 006 Raw Data.en.srt | 8.2 KB | ||
| 006 Raw Data.mp4 | 20.5 MB | ||
| 006 Robust Scaler Applied to Dataset.en.srt | 2.2 KB | ||
| 006 Robust Scaler Applied to Dataset.mp4 | 8.4 MB | ||
| 007 Explore Robust Scaler Range.en.srt | 1.6 KB | ||
| 007 Explore Robust Scaler Range.mp4 | 5.6 MB | ||
| 007 Machine Learning is Mostly Data Preparation.en.srt | 4.1 KB | ||
| 007 Machine Learning is Mostly Data Preparation.mp4 | 40.9 MB | ||
| 007 Mutual Information.en.srt | 2.2 KB | ||
| 007 Mutual Information.mp4 | 6.9 MB | ||
| 007 Remove Outliers - The Standard Deviation Approach.en.srt | 5.4 KB | ||
| 007 Remove Outliers - The Standard Deviation Approach.mp4 | 18.5 MB | ||
| 007 Save Model and Data Scaler.en.srt | 3.8 KB | ||
| 007 Save Model and Data Scaler.mp4 | 15.2 MB | ||
| 008 Common Data Preparation Tasks - Data Cleansing.en.srt | 3.7 KB | ||
| 008 Common Data Preparation Tasks - Data Cleansing.mp4 | 21.7 MB | ||
| 008 Load and Apply Saved Scalers.en.srt | 2 KB | ||
| 008 Load and Apply Saved Scalers.mp4 | 6.6 MB | ||
| 008 Modeling with Selected Categorical Features.en.srt | 4 KB | ||
| 008 Modeling with Selected Categorical Features.mp4 | 14.1 MB | ||
| 008 Nominal and Ordinal Variables.en.srt | 4.4 KB | ||
| 008 Nominal and Ordinal Variables.mp4 | 26 MB | ||
| 008 Remove Outliers - The IQR Approach.en.srt | 3.8 KB | ||
| 008 Remove Outliers - The IQR Approach.mp4 | 14.9 MB | ||
| 009 Automatic Outlier Detection.en.srt | 5.2 KB | ||
| 009 Automatic Outlier Detection.mp4 | 18.6 MB | ||
| 009 Common Data Preparation Tasks - Feature Selection.en.srt | 3.5 KB | ||
| 009 Common Data Preparation Tasks - Feature Selection.mp4 | 7.9 MB | ||
| 009 Feature Selection with ANOVA on Numerical Input.en.srt | 6.4 KB | ||
| 009 Feature Selection with ANOVA on Numerical Input.mp4 | 17.2 MB | ||
| 009 Ordinal Encoding.en.srt | 3.3 KB | ||
| 009 Ordinal Encoding.mp4 | 7 MB | ||
| 010 Common Data Preparation Tasks - Data Transforms.en.srt | 3.9 KB | ||
| 010 Common Data Preparation Tasks - Data Transforms.mp4 | 4.7 MB | ||
| 010 Feature Selection with Mutual Information.en.srt | 2.7 KB | ||
| 010 Feature Selection with Mutual Information.mp4 | 7.3 MB | ||
| 010 Mark Missing Values.en.srt | 6.8 KB | ||
| 010 Mark Missing Values.mp4 | 22.7 MB | ||
| 010 One-Hot Encoding Defined.en.srt | 1.3 KB | ||
| 010 One-Hot Encoding Defined.mp4 | 1.7 MB | ||
| 011 Common Data Preparation Tasks - Feature Engineering.en.srt | 2.2 KB | ||
| 011 Common Data Preparation Tasks - Feature Engineering.mp4 | 21.6 MB | ||
| 011 Modeling with Selected Numerical Features.en.srt | 2.6 KB | ||
| 011 Modeling with Selected Numerical Features.mp4 | 9.7 MB | ||
| 011 One-Hot Encoding.en.srt | 2.9 KB | ||
| 011 One-Hot Encoding.mp4 | 6.8 MB | ||
| 011 Remove Rows with Missing Values.en.srt | 2.4 KB | ||
| 011 Remove Rows with Missing Values.mp4 | 10 MB | ||
| 012 Common Data Preparation Tasks - Dimensionality Reduction.en.srt | 2.9 KB | ||
| 012 Common Data Preparation Tasks - Dimensionality Reduction.mp4 | 4.1 MB | ||
| 012 Dummy Variable Encoding.en.srt | 3.1 KB | ||
| 012 Dummy Variable Encoding.mp4 | 7 MB | ||
| 012 Statistical Imputation.en.srt | 2 KB | ||
| 012 Statistical Imputation.mp4 | 2.6 MB | ||
| 012 Tuning Number of Selected Features.en.srt | 3.9 KB | ||
| 012 Tuning Number of Selected Features.mp4 | 14.4 MB | ||
| 013 Data Leakage.en.srt | 1.1 KB | ||
| 013 Data Leakage.mp4 | 8.8 MB | ||
| 013 Mean Value Imputation.en.srt | 4.9 KB | ||
| 013 Mean Value Imputation.mp4 | 15.9 MB | ||
| 013 OrdinalEncoder Transform on Breast Cancer Dataset.en.srt | 5 KB | ||
| 013 OrdinalEncoder Transform on Breast Cancer Dataset.mp4 | 17.1 MB | ||
| 013 Select Features for Numerical Output.en.srt | 3.4 KB | ||
| 013 Select Features for Numerical Output.mp4 | 8.7 MB | ||
| 014 Linear Correlation with Correlation Statistics.en.srt | 3.3 KB | ||
| 014 Linear Correlation with Correlation Statistics.mp4 | 9.9 MB | ||
| 014 Make Distributions More Gaussian.en.srt | 2.9 KB | ||
| 014 Make Distributions More Gaussian.mp4 | 4 MB | ||
| 014 Problem With Naïve Data Preparation.en.srt | 5.2 KB | ||
| 014 Problem With Naïve Data Preparation.mp4 | 24.9 MB | ||
| 014 Simple Imputer with Model Evaluation.en.srt | 1.8 KB | ||
| 014 Simple Imputer with Model Evaluation.mp4 | 7.6 MB | ||
| 015 Case Study_ Data Leakage_ Train_Test_Split Naïve Approach.en.srt | 3.9 KB | ||
| 015 Case Study_ Data Leakage_ Train_Test_Split Naïve Approach.mp4 | 16.5 MB | ||
| 015 Compare Different Statistical Imputation Strategies.en.srt | 2.5 KB | ||
| 015 Compare Different Statistical Imputation Strategies.mp4 | 9.3 MB | ||
| 015 Linear Correlation with Mutual Information.en.srt | 3.1 KB | ||
| 015 Linear Correlation with Mutual Information.mp4 | 10.8 MB | ||
| 015 Power Transform on Contrived Dataset.en.srt | 3.6 KB | ||
| 015 Power Transform on Contrived Dataset.mp4 | 8.5 MB | ||
| 016 Baseline and Model Built Using Correlation.en.srt | 3.1 KB | ||
| 016 Baseline and Model Built Using Correlation.mp4 | 13.1 MB | ||
| 016 Case Study_ Data Leakage_ Train_Test_Split Correct Approach.en.srt | 2.4 KB | ||
| 016 Case Study_ Data Leakage_ Train_Test_Split Correct Approach.mp4 | 9.5 MB | ||
| 016 K-Nearest Neighbors Imputation.en.srt | 5.1 KB | ||
| 016 K-Nearest Neighbors Imputation.mp4 | 16.9 MB | ||
| 016 Power Transform on Sonar Dataset.en.srt | 2.9 KB | ||
| 016 Power Transform on Sonar Dataset.mp4 | 10.9 MB | ||
| 017 Box-Cox on Sonar Dataset.en.srt | 3.1 KB | ||
| 017 Box-Cox on Sonar Dataset.mp4 | 11.7 MB | ||
| 017 Case Study_ Data Leakage_ K-Fold Naïve Approach.en.srt | 4.2 KB | ||
| 017 Case Study_ Data Leakage_ K-Fold Naïve Approach.mp4 | 14.3 MB | ||
| 017 KNNImputer and Model Evaluation.en.srt | 3.4 KB | ||
| 017 KNNImputer and Model Evaluation.mp4 | 12.9 MB | ||
| 017 Model Built Using Mutual Information Features.en.srt | 1 KB | ||
| 017 Model Built Using Mutual Information Features.mp4 | 3.9 MB | ||
| 018 Case Study_ Data Leakage_ K-Fold Correct Approach.en.srt | 3.1 KB | ||
| 018 Case Study_ Data Leakage_ K-Fold Correct Approach.mp4 | 12.8 MB | ||
| 018 Iterative Imputation.en.srt | 4.1 KB | ||
| 018 Iterative Imputation.mp4 | 13.8 MB | ||
| 018 Tuning Number of Selected Features.en.srt | 4.8 KB | ||
| 018 Tuning Number of Selected Features.mp4 | 20.3 MB | ||
| 018 Yeo-Johnson on Sonar Dataset.en.srt | 2.7 KB | ||
| 018 Yeo-Johnson on Sonar Dataset.mp4 | 9.6 MB | ||
| 019 IterativeImputer and Model Evaluation.en.srt | 1.4 KB | ||
| 019 IterativeImputer and Model Evaluation.mp4 | 6.5 MB | ||
| 019 Polynomial Features.en.srt | 5.2 KB | ||
| 019 Polynomial Features.mp4 | 20.7 MB | ||
| 019 Recursive Feature Elimination.en.srt | 3.8 KB | ||
| 019 Recursive Feature Elimination.mp4 | 27.9 MB | ||
| 020 IterativeImputer and Different Imputation Order.en.srt | 2.2 KB | ||
| 020 IterativeImputer and Different Imputation Order.mp4 | 8.4 MB | ||
| 020 Polynomial Transform on Sonar Dataset.en.srt | 5.2 KB | ||
| 020 Polynomial Transform on Sonar Dataset.mp4 | 20.6 MB | ||
| 020 RFE for Classification.en.srt | 4.6 KB | ||
| 020 RFE for Classification.mp4 | 18.5 MB | ||
| 021 Effect of Polynomial Degrees.en.srt | 2.7 KB | ||
| 021 Effect of Polynomial Degrees.mp4 | 7.5 MB | ||
| 021 RFE for Regression.en.srt | 2.6 KB | ||
| 021 RFE for Regression.mp4 | 9.4 MB | ||
| 022 RFE Hyperparameters.en.srt | 3.4 KB | ||
| 022 RFE Hyperparameters.mp4 | 12.1 MB | ||
| 023 Feature Ranking for RFE.en.srt | 3 KB | ||
| 023 Feature Ranking for RFE.mp4 | 10.9 MB | ||
| 024 Feature Importance Scores Defined.en.srt | 3.9 KB | ||
| 024 Feature Importance Scores Defined.mp4 | 26.2 MB | ||
| 025 Feature Importance Scores_ Linear Regression.en.srt | 4.3 KB | ||
| 025 Feature Importance Scores_ Linear Regression.mp4 | 13.3 MB | ||
| 026 Feature Importance Scores_ Logistic Regression and CART.en.srt | 4.4 KB | ||
| 026 Feature Importance Scores_ Logistic Regression and CART.mp4 | 14.2 MB | ||
| 027 Feature Importance Scores_ Random Forests.en.srt | 1.9 KB | ||
| 027 Feature Importance Scores_ Random Forests.mp4 | 6.6 MB | ||
| 028 Permutation Feature Importance.en.srt | 3.1 KB | ||
| 028 Permutation Feature Importance.mp4 | 10.8 MB | ||
| 029 Feature Selection with Importance.en.srt | 4.4 KB | ||
| 029 Feature Selection with Importance.mp4 | 15.5 MB | ||
| 030 housing.csv | 47.9 KB | ||
| 094 abalone.csv | 187.4 KB | ||
| Advanced Transforms.ipynb | 47.8 KB | ||
| Automatic Outlier Detection.ipynb | 4.5 KB | ||
| Bonus Resources.txt | 307.2 B | ||
| Categorical Feature Selection.ipynb | 28.8 KB | ||
| Choosing Numerical Input Features.ipynb | 36.7 KB | ||
| Comparing Different Imputed Statistics.ipynb | 12.3 KB | ||
| Data Cleansing Master Class - Data Preparation With Training and Testing Sets.ipynb | 6.6 KB | ||
| Data Rescaling .ipynb | 60.4 KB | ||
| Dimensionality Reduction.ipynb | 34.8 KB | ||
| Feature Importance Scores.ipynb | 57.8 KB | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| Identify and Remove Duplicate Rows.ipynb | 1.8 KB | ||
| IterativeImputer Data Transform.ipynb | 2.4 KB | ||
| IterativeImputer and Different Number of Iterations.ipynb | 14.1 KB | ||
| IterativeImputer and Model Evaluation.ipynb | 2.4 KB | ||
| KNNImputer and Model Evaluation Different K-Values.ipynb | 13.6 KB | ||
| Mark Missing Values.ipynb | 11.3 KB | ||
| Outlier Removal - IQR Approach.ipynb | 1.8 KB | ||
| Outlier Removal - Standard Deviation Approach.ipynb | 2.3 KB | ||
| Polynomial Feature Transform.ipynb | 26.9 KB | ||
| Power Transforms.ipynb | 85.1 KB | ||
| Remove Missing Values.ipynb | 9.4 KB | ||
| Select Features for Numerical Output.ipynb | 44.1 KB | ||
| SimpleImputer and Model Evaluation.ipynb | 2 KB | ||
| Sparse Column Identification and Removal.ipynb | 20.6 KB | ||
| Statistical Imputation With KNN.ipynb | 7.2 KB | ||
| Statistical Imputation With SimpleImputer.ipynb | 7.2 KB | ||
| ▲ 237 total files | |||
Data Cleansing Master Class in Python
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 104 lectures (3h 31m) | Size: 1.12 GB
The Complete Guide to Data Cleansing for Machine Learning Engineers
What you'll learn:
You'll learn data imputation and advanced data cleansing techniques.
You'll learn how to apply real-world data cleansing techniques to your data.
You'll learn advanced data cleansing techniques.
You'll learn how to prepare data in a way that avoids data leakage, and in turn, incorrect model evaluation.
Requirements
You'll need a really solid foundation in Python.
You'll need to understand the basics of machine learning.
Description
Welcome to Data Cleansing Master Class in Python.
Data preparation may be the most important part of a machine learning project. It is the most time consuming part, although it seems to be the least discussed topic. Data preparation, sometimes referred to as data preprocessing, is the act of transforming raw data into a form that is appropriate for modeling.
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