|
Обратите внимание, что наш сайт не размещает какие-либо файлы из списка. Вы не можете скачать
эти файлы или скачать torrent-файл.
|
| [TGx]Downloaded from torrentgalaxy.to .txt |
585б |
| 0 |
68б |
| 001 Course Introduction.en.srt |
2.82Кб |
| 001 Course Introduction.mp4 |
22.61Мб |
| 001 Curse of Dimensionality.en.srt |
2.66Кб |
| 001 Curse of Dimensionality.mp4 |
6.18Мб |
| 001 Data Cleansing Overview.en.srt |
2.15Кб |
| 001 Data Cleansing Overview.mp4 |
20.01Мб |
| 001 Feature Selection Introduction.en.srt |
2.39Кб |
| 001 Feature Selection Introduction.mp4 |
19.54Мб |
| 001 Introducing Data Preparation.en.srt |
2.80Кб |
| 001 Introducing Data Preparation.mp4 |
36.45Мб |
| 001 Scale Numerical Data.en.srt |
2.72Кб |
| 001 Scale Numerical Data.mp4 |
5.07Мб |
| 001 Transforming Different Data Types.en.srt |
3.15Кб |
| 001 Transforming Different Data Types.mp4 |
8.89Мб |
| 002 Course Structure.en.srt |
3.57Кб |
| 002 Course Structure.mp4 |
23.85Мб |
| 002 Diabetes Dataset for Scaling.en.srt |
2.46Кб |
| 002 Diabetes Dataset for Scaling.mp4 |
8.68Мб |
| 002 Feature Selection Defined.en.srt |
4.38Кб |
| 002 Feature Selection Defined.mp4 |
5.21Мб |
| 002 Identify Columns That Contain a Single Value.en.srt |
3.21Кб |
| 002 Identify Columns That Contain a Single Value.mp4 |
7.49Мб |
| 002 Techniques for Dimensionality Reduction.en.srt |
4.91Кб |
| 002 Techniques for Dimensionality Reduction.mp4 |
12.96Мб |
| 002 The ColumnTransformer.en.srt |
3.13Кб |
| 002 The ColumnTransformer.mp4 |
10.49Мб |
| 002 The Machine Learning Process.en.srt |
5.40Кб |
| 002 The Machine Learning Process.mp4 |
14.27Мб |
| 003 Data Preparation Defined.en.srt |
3.82Кб |
| 003 Data Preparation Defined.mp4 |
30.23Мб |
| 003 Identify Columns with Few Values.en.srt |
4.23Кб |
| 003 Identify Columns with Few Values.mp4 |
12.01Мб |
| 003 Is this Course Right for You_.en.srt |
1.74Кб |
| 003 Is this Course Right for You_.mp4 |
1.59Мб |
| 003 Linear Discriminant Analysis.en.srt |
3.00Кб |
| 003 Linear Discriminant Analysis.mp4 |
7.64Мб |
| 003 MinMaxScaler Transform.en.srt |
2.33Кб |
| 003 MinMaxScaler Transform.mp4 |
8.94Мб |
| 003 Statistics for Feature Selection.en.srt |
2.97Кб |
| 003 Statistics for Feature Selection.mp4 |
9.51Мб |
| 003 The ColumnTransformer on Abalone Dataset.en.srt |
3.70Кб |
| 003 The ColumnTransformer on Abalone Dataset.mp4 |
13.06Мб |
| 004 Choosing a Data Preparation Technique.en.srt |
2.69Кб |
| 004 Choosing a Data Preparation Technique.mp4 |
25.89Мб |
| 004 Linear Discriminant Analysis Demonstrated.en.srt |
5.30Кб |
| 004 Linear Discriminant Analysis Demonstrated.mp4 |
18.64Мб |
| 004 Loading a Categorical Dataset.en.srt |
3.37Кб |
| 004 Loading a Categorical Dataset.mp4 |
10.32Мб |
| 004 Manually Transform Target Variable.en.srt |
3.44Кб |
| 004 Manually Transform Target Variable.mp4 |
13.23Мб |
| 004 Remove Columns with Low Variance.en.srt |
3.84Кб |
| 004 Remove Columns with Low Variance.mp4 |
11.15Мб |
| 004 StandardScaler Transform.en.srt |
2.58Кб |
| 004 StandardScaler Transform.mp4 |
10.49Мб |
| 005 Automatically Transform Target Variable.en.srt |
5.44Кб |
| 005 Automatically Transform Target Variable.mp4 |
20.45Мб |
| 005 Encode the Dataset for Modeling.en.srt |
3.12Кб |
| 005 Encode the Dataset for Modeling.mp4 |
9.43Мб |
| 005 Identify and Remove Rows That Contain Duplicate Data.en.srt |
3.92Кб |
| 005 Identify and Remove Rows That Contain Duplicate Data.mp4 |
15.63Мб |
| 005 Principal Component Analysis.en.srt |
7.20Кб |
| 005 Principal Component Analysis.mp4 |
22.64Мб |
| 005 Robust Scaling Data.en.srt |
5.58Кб |
| 005 Robust Scaling Data.mp4 |
16.53Мб |
| 005 What is Data in Machine Learning_.en.srt |
4.74Кб |
| 005 What is Data in Machine Learning_.mp4 |
17.88Мб |
| 006 Challenge of Preparing New Data for a Model.en.srt |
4.85Кб |
| 006 Challenge of Preparing New Data for a Model.mp4 |
34.07Мб |
| 006 Chi-Squared.en.srt |
2.98Кб |
| 006 Chi-Squared.mp4 |
7.02Мб |
| 006 Defining Outliers.en.srt |
2.68Кб |
| 006 Defining Outliers.mp4 |
14.36Мб |
| 006 Raw Data.en.srt |
8.16Кб |
| 006 Raw Data.mp4 |
20.51Мб |
| 006 Robust Scaler Applied to Dataset.en.srt |
2.16Кб |
| 006 Robust Scaler Applied to Dataset.mp4 |
8.42Мб |
| 007 Explore Robust Scaler Range.en.srt |
1.61Кб |
| 007 Explore Robust Scaler Range.mp4 |
5.62Мб |
| 007 Machine Learning is Mostly Data Preparation.en.srt |
4.11Кб |
| 007 Machine Learning is Mostly Data Preparation.mp4 |
40.89Мб |
| 007 Mutual Information.en.srt |
2.18Кб |
| 007 Mutual Information.mp4 |
6.92Мб |
| 007 Remove Outliers - The Standard Deviation Approach.en.srt |
5.41Кб |
| 007 Remove Outliers - The Standard Deviation Approach.mp4 |
18.55Мб |
| 007 Save Model and Data Scaler.en.srt |
3.84Кб |
| 007 Save Model and Data Scaler.mp4 |
15.23Мб |
| 008 Common Data Preparation Tasks - Data Cleansing.en.srt |
3.72Кб |
| 008 Common Data Preparation Tasks - Data Cleansing.mp4 |
21.71Мб |
| 008 Load and Apply Saved Scalers.en.srt |
2.00Кб |
| 008 Load and Apply Saved Scalers.mp4 |
6.60Мб |
| 008 Modeling with Selected Categorical Features.en.srt |
4.00Кб |
| 008 Modeling with Selected Categorical Features.mp4 |
14.10Мб |
| 008 Nominal and Ordinal Variables.en.srt |
4.36Кб |
| 008 Nominal and Ordinal Variables.mp4 |
25.96Мб |
| 008 Remove Outliers - The IQR Approach.en.srt |
3.77Кб |
| 008 Remove Outliers - The IQR Approach.mp4 |
14.90Мб |
| 009 Automatic Outlier Detection.en.srt |
5.18Кб |
| 009 Automatic Outlier Detection.mp4 |
18.60Мб |
| 009 Common Data Preparation Tasks - Feature Selection.en.srt |
3.53Кб |
| 009 Common Data Preparation Tasks - Feature Selection.mp4 |
7.91Мб |
| 009 Feature Selection with ANOVA on Numerical Input.en.srt |
6.43Кб |
| 009 Feature Selection with ANOVA on Numerical Input.mp4 |
17.22Мб |
| 009 Ordinal Encoding.en.srt |
3.30Кб |
| 009 Ordinal Encoding.mp4 |
7.04Мб |
| 010 Common Data Preparation Tasks - Data Transforms.en.srt |
3.89Кб |
| 010 Common Data Preparation Tasks - Data Transforms.mp4 |
4.69Мб |
| 010 Feature Selection with Mutual Information.en.srt |
2.70Кб |
| 010 Feature Selection with Mutual Information.mp4 |
7.25Мб |
| 010 Mark Missing Values.en.srt |
6.84Кб |
| 010 Mark Missing Values.mp4 |
22.68Мб |
| 010 One-Hot Encoding Defined.en.srt |
1.33Кб |
| 010 One-Hot Encoding Defined.mp4 |
1.70Мб |
| 011 Common Data Preparation Tasks - Feature Engineering.en.srt |
2.16Кб |
| 011 Common Data Preparation Tasks - Feature Engineering.mp4 |
21.57Мб |
| 011 Modeling with Selected Numerical Features.en.srt |
2.63Кб |
| 011 Modeling with Selected Numerical Features.mp4 |
9.67Мб |
| 011 One-Hot Encoding.en.srt |
2.87Кб |
| 011 One-Hot Encoding.mp4 |
6.84Мб |
| 011 Remove Rows with Missing Values.en.srt |
2.42Кб |
| 011 Remove Rows with Missing Values.mp4 |
10.00Мб |
| 012 Common Data Preparation Tasks - Dimensionality Reduction.en.srt |
2.94Кб |
| 012 Common Data Preparation Tasks - Dimensionality Reduction.mp4 |
4.07Мб |
| 012 Dummy Variable Encoding.en.srt |
3.09Кб |
| 012 Dummy Variable Encoding.mp4 |
6.96Мб |
| 012 Statistical Imputation.en.srt |
1.97Кб |
| 012 Statistical Imputation.mp4 |
2.59Мб |
| 012 Tuning Number of Selected Features.en.srt |
3.90Кб |
| 012 Tuning Number of Selected Features.mp4 |
14.38Мб |
| 013 Data Leakage.en.srt |
1.14Кб |
| 013 Data Leakage.mp4 |
8.82Мб |
| 013 Mean Value Imputation.en.srt |
4.95Кб |
| 013 Mean Value Imputation.mp4 |
15.90Мб |
| 013 OrdinalEncoder Transform on Breast Cancer Dataset.en.srt |
5.01Кб |
| 013 OrdinalEncoder Transform on Breast Cancer Dataset.mp4 |
17.13Мб |
| 013 Select Features for Numerical Output.en.srt |
3.38Кб |
| 013 Select Features for Numerical Output.mp4 |
8.74Мб |
| 014 Linear Correlation with Correlation Statistics.en.srt |
3.29Кб |
| 014 Linear Correlation with Correlation Statistics.mp4 |
9.91Мб |
| 014 Make Distributions More Gaussian.en.srt |
2.92Кб |
| 014 Make Distributions More Gaussian.mp4 |
3.96Мб |
| 014 Problem With Naïve Data Preparation.en.srt |
5.24Кб |
| 014 Problem With Naïve Data Preparation.mp4 |
24.85Мб |
| 014 Simple Imputer with Model Evaluation.en.srt |
1.82Кб |
| 014 Simple Imputer with Model Evaluation.mp4 |
7.56Мб |
| 015 Case Study_ Data Leakage_ Train_Test_Split Naïve Approach.en.srt |
3.90Кб |
| 015 Case Study_ Data Leakage_ Train_Test_Split Naïve Approach.mp4 |
16.53Мб |
| 015 Compare Different Statistical Imputation Strategies.en.srt |
2.52Кб |
| 015 Compare Different Statistical Imputation Strategies.mp4 |
9.28Мб |
| 015 Linear Correlation with Mutual Information.en.srt |
3.07Кб |
| 015 Linear Correlation with Mutual Information.mp4 |
10.83Мб |
| 015 Power Transform on Contrived Dataset.en.srt |
3.56Кб |
| 015 Power Transform on Contrived Dataset.mp4 |
8.55Мб |
| 016 Baseline and Model Built Using Correlation.en.srt |
3.06Кб |
| 016 Baseline and Model Built Using Correlation.mp4 |
13.13Мб |
| 016 Case Study_ Data Leakage_ Train_Test_Split Correct Approach.en.srt |
2.35Кб |
| 016 Case Study_ Data Leakage_ Train_Test_Split Correct Approach.mp4 |
9.53Мб |
| 016 K-Nearest Neighbors Imputation.en.srt |
5.07Кб |
| 016 K-Nearest Neighbors Imputation.mp4 |
16.87Мб |
| 016 Power Transform on Sonar Dataset.en.srt |
2.86Кб |
| 016 Power Transform on Sonar Dataset.mp4 |
10.91Мб |
| 017 Box-Cox on Sonar Dataset.en.srt |
3.06Кб |
| 017 Box-Cox on Sonar Dataset.mp4 |
11.71Мб |
| 017 Case Study_ Data Leakage_ K-Fold Naïve Approach.en.srt |
4.16Кб |
| 017 Case Study_ Data Leakage_ K-Fold Naïve Approach.mp4 |
14.32Мб |
| 017 KNNImputer and Model Evaluation.en.srt |
3.42Кб |
| 017 KNNImputer and Model Evaluation.mp4 |
12.87Мб |
| 017 Model Built Using Mutual Information Features.en.srt |
1.01Кб |
| 017 Model Built Using Mutual Information Features.mp4 |
3.92Мб |
| 018 Case Study_ Data Leakage_ K-Fold Correct Approach.en.srt |
3.14Кб |
| 018 Case Study_ Data Leakage_ K-Fold Correct Approach.mp4 |
12.76Мб |
| 018 Data Cleansing Master Class - Data Preparation With Training and Testing Sets.zip |
1.47Кб |
| 018 Iterative Imputation.en.srt |
4.10Кб |
| 018 Iterative Imputation.mp4 |
13.82Мб |
| 018 Tuning Number of Selected Features.en.srt |
4.84Кб |
| 018 Tuning Number of Selected Features.mp4 |
20.33Мб |
| 018 Yeo-Johnson on Sonar Dataset.en.srt |
2.67Кб |
| 018 Yeo-Johnson on Sonar Dataset.mp4 |
9.62Мб |
| 019 IterativeImputer and Model Evaluation.en.srt |
1.45Кб |
| 019 IterativeImputer and Model Evaluation.mp4 |
6.52Мб |
| 019 Polynomial Features.en.srt |
5.21Кб |
| 019 Polynomial Features.mp4 |
20.67Мб |
| 019 Recursive Feature Elimination.en.srt |
3.76Кб |
| 019 Recursive Feature Elimination.mp4 |
27.94Мб |
| 020 IterativeImputer and Different Imputation Order.en.srt |
2.18Кб |
| 020 IterativeImputer and Different Imputation Order.mp4 |
8.41Мб |
| 020 Polynomial Transform on Sonar Dataset.en.srt |
5.21Кб |
| 020 Polynomial Transform on Sonar Dataset.mp4 |
20.63Мб |
| 020 RFE for Classification.en.srt |
4.60Кб |
| 020 RFE for Classification.mp4 |
18.49Мб |
| 021 Effect of Polynomial Degrees.en.srt |
2.71Кб |
| 021 Effect of Polynomial Degrees.mp4 |
7.48Мб |
| 021 RFE for Regression.en.srt |
2.58Кб |
| 021 RFE for Regression.mp4 |
9.40Мб |
| 022 RFE Hyperparameters.en.srt |
3.44Кб |
| 022 RFE Hyperparameters.mp4 |
12.05Мб |
| 023 Feature Ranking for RFE.en.srt |
2.98Кб |
| 023 Feature Ranking for RFE.mp4 |
10.93Мб |
| 023 Sparse Column Identification and Removal.zip |
10.15Кб |
| 024 Feature Importance Scores Defined.en.srt |
3.93Кб |
| 024 Feature Importance Scores Defined.mp4 |
26.19Мб |
| 025 Feature Importance Scores_ Linear Regression.en.srt |
4.34Кб |
| 025 Feature Importance Scores_ Linear Regression.mp4 |
13.29Мб |
| 026 Feature Importance Scores_ Logistic Regression and CART.en.srt |
4.43Кб |
| 026 Feature Importance Scores_ Logistic Regression and CART.mp4 |
14.19Мб |
| 026 Identify and Remove Duplicate Rows.zip |
824б |
| 027 Feature Importance Scores_ Random Forests.en.srt |
1.95Кб |
| 027 Feature Importance Scores_ Random Forests.mp4 |
6.59Мб |
| 028 Outlier Removal - Standard Deviation Approach.zip |
951б |
| 028 Permutation Feature Importance.en.srt |
3.09Кб |
| 028 Permutation Feature Importance.mp4 |
10.78Мб |
| 029 Feature Selection with Importance.en.srt |
4.38Кб |
| 029 Feature Selection with Importance.mp4 |
15.48Мб |
| 029 Outlier Removal - IQR Approach.zip |
920б |
| 030 Automatic Outlier Detection.zip |
1.20Кб |
| 030 housing.csv |
47.93Кб |
| 031 Mark Missing Values.zip |
2.58Кб |
| 032 Remove Missing Values.zip |
1.59Кб |
| 034 Statistical Imputation With SimpleImputer.zip |
1.72Кб |
| 035 SimpleImputer and Model Evaluation.zip |
1.02Кб |
| 036 Comparing Different Imputed Statistics.zip |
7.35Кб |
| 037 Statistical Imputation With KNN.zip |
1.69Кб |
| 038 KNNImputer and Model Evaluation Different K-Values.zip |
7.97Кб |
| 039 IterativeImputer Data Transform.zip |
997б |
| 040 IterativeImputer and Model Evaluation.zip |
1.05Кб |
| 041 IterativeImputer and Different Number of Iterations.zip |
8.28Кб |
| 045 Categorical Feature Selection.zip |
8.78Кб |
| 050 Choosing Numerical Input Features.zip |
15.60Кб |
| 054 Select Features for Numerical Output.zip |
17.82Кб |
| 066 Feature Importance Scores.zip |
26.95Кб |
| 072 Data Rescaling .zip |
25.02Кб |
| 085 Power Transforms.zip |
50.36Кб |
| 089 Polynomial Feature Transform.zip |
14.18Кб |
| 092 Advanced Transforms.zip |
6.12Кб |
| 094 abalone.csv |
187.38Кб |
| 1 |
556б |
| 10 |
326.50Кб |
| 100 |
82.77Кб |
| 100 Dimensionality Reduction.zip |
18.51Кб |
| 101 |
420.54Кб |
| 102 |
309.33Кб |
| 11 |
367.22Кб |
| 12 |
402.71Кб |
| 13 |
292.14Кб |
| 14 |
442.24Кб |
| 15 |
333.19Кб |
| 16 |
380.35Кб |
| 17 |
501.27Кб |
| 18 |
55.92Кб |
| 19 |
170.00Кб |
| 2 |
281.87Кб |
| 20 |
498.14Кб |
| 21 |
474.19Кб |
| 22 |
373.28Кб |
| 23 |
404.84Кб |
| 24 |
462.45Кб |
| 25 |
5.36Кб |
| 26 |
125.49Кб |
| 27 |
282.09Кб |
| 28 |
377.82Кб |
| 29 |
136.45Кб |
| 3 |
280.83Кб |
| 30 |
478.65Кб |
| 31 |
484.81Кб |
| 32 |
104.88Кб |
| 33 |
375.81Кб |
| 34 |
24.76Кб |
| 35 |
278.76Кб |
| 36 |
97.42Кб |
| 37 |
120.42Кб |
| 38 |
140.53Кб |
| 39 |
179.21Кб |
| 4 |
59.71Кб |
| 40 |
233.34Кб |
| 41 |
319.63Кб |
| 42 |
412.09Кб |
| 43 |
186.61Кб |
| 44 |
211.47Кб |
| 45 |
280.39Кб |
| 46 |
382.60Кб |
| 47 |
449.02Кб |
| 48 |
41.44Кб |
| 49 |
136.97Кб |
| 5 |
313.43Кб |
| 50 |
245.75Кб |
| 51 |
459.10Кб |
| 52 |
505.90Кб |
| 53 |
295.42Кб |
| 54 |
353.54Кб |
| 55 |
68.44Кб |
| 56 |
89.68Кб |
| 57 |
172.67Кб |
| 58 |
225.58Кб |
| 59 |
5.73Кб |
| 6 |
42.12Кб |
| 60 |
7.33Кб |
| 61 |
187.23Кб |
| 62 |
4.21Кб |
| 63 |
91.57Кб |
| 64 |
336.28Кб |
| 65 |
389.93Кб |
| 66 |
486.27Кб |
| 67 |
499.05Кб |
| 68 |
69.31Кб |
| 69 |
101.20Кб |
| 7 |
109.05Кб |
| 70 |
229.68Кб |
| 71 |
59.80Кб |
| 72 |
111.30Кб |
| 73 |
186.73Кб |
| 74 |
262.25Кб |
| 75 |
322.91Кб |
| 76 |
464.60Кб |
| 77 |
77.22Кб |
| 78 |
93.38Кб |
| 79 |
89.82Кб |
| 8 |
153.57Кб |
| 80 |
371.15Кб |
| 81 |
450.20Кб |
| 82 |
13.22Кб |
| 83 |
20.31Кб |
| 84 |
254.56Кб |
| 85 |
470.17Кб |
| 86 |
493.80Кб |
| 87 |
38.90Кб |
| 88 |
84.09Кб |
| 89 |
160.75Кб |
| 9 |
152.92Кб |
| 90 |
408.67Кб |
| 91 |
418.13Кб |
| 92 |
491.90Кб |
| 93 |
332.63Кб |
| 94 |
392.58Кб |
| 95 |
295.66Кб |
| 96 |
435.50Кб |
| 97 |
316.74Кб |
| 98 |
445.41Кб |
| 99 |
37.93Кб |
| TutsNode.com.txt |
63б |