download скачать Free download скачать : Udemy Data Science in Python Classification Modeling BOOKWARE-SOFTiMAGE
mp4 | Video: h264,1280X720 | Audio: AAC, 44.1 KHz
Genre:eLearning | Language: English | Size:3.36 GB

Files Included :

1  Course Introduction.mp4 (29.33 MB)
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2  About This Series.mp4 (3.65 MB)
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3  Course Structure & Outline.mp4 (11.83 MB)
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4  Course Structure & Outline.mp4 (11.83 MB)
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7  Introducing the Course Project.mp4 (4.47 MB)
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8  Setting Expectations.mp4 (10.1 MB)
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9  Jupyter Installation & Launch.mp4 (23.83 MB)
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1  Decision Trees.mp4 (18.02 MB)
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10  DEMO Hyperparameter Tuning.mp4 (22.86 MB)
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11  ASSIGNMENT Tuned Decision Tree.mp4 (3.55 MB)
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12  SOLUTION Tuned Decision Tree.mp4 (26.58 MB)
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13  Pros & Cons of Decision Trees.mp4 (16.66 MB)
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14  Key Takeaways.mp4 (6.45 MB)
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2  Entropy.mp4 (24 MB)
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3  Decision Tree Predictions.mp4 (20.83 MB)
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4  Decision Trees in Python.mp4 (15.84 MB)
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5  DEMO Decision Trees.mp4 (32.33 MB)
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6  Feature Importance.mp4 (28.29 MB)
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7  ASSIGNMENT Decision Trees.mp4 (5.66 MB)
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8  SOLUTION Decision Trees.mp4 (35.14 MB)
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9  Hyperparameter Tuning for Decision Trees.mp4 (26.21 MB)
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1  Ensemble Models.mp4 (20.89 MB)
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10  Pros & Cons of Random Forests.mp4 (10.82 MB)
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11  ASSIGNMENT Random Forests.mp4 (5.17 MB)
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12  SOLUTION Random Forests.mp4 (43.63 MB)
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13  Gradient Boosting.mp4 (9.61 MB)
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14  Gradient Boosting in Python.mp4 (12.4 MB)
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15  Hyperparameter Tuning for Gradient Boosting.mp4 (28.53 MB)
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16  DEMO Hyperparameter Tuning for Gradient Boosting.mp4 (22.92 MB)
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17  Pros & Cons of Gradient Boosting.mp4 (10.74 MB)
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18  ASSIGNMENT Gradient Boosting.mp4 (4.94 MB)
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19  SOLUTION Gradient Boosting.mp4 (30.41 MB)
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2  Simple Ensemble Models.mp4 (12.34 MB)
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20  PRO TIP SHAP Values.mp4 (32.12 MB)
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21  DEMO SHAP Values.mp4 (28.72 MB)
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22  Key Takeaways.mp4 (6.35 MB)
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3  DEMO Simple Ensemble Models.mp4 (26.33 MB)
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4  ASSIGNMENT Simple Ensemble Models.mp4 (8.2 MB)
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5  SOLUTION Simple Ensemble Models.mp4 (27.41 MB)
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6  Random Forests.mp4 (6.3 MB)
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7  Fitting Random Forests in Python.mp4 (21.7 MB)
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8  Hyperparameter Tuning for Random Forests.mp4 (28.25 MB)
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9  PRO TIP Random Search.mp4 (36.68 MB)
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1  Recap Classification Models & Workflow.mp4 (13.96 MB)
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2  Pros & Cons of Classification Models.mp4 (20.18 MB)
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3  DEMO Production Pipeline & Deployment.mp4 (75.26 MB)
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4  Looking Ahead Unsupervised Learning.mp4 (5.53 MB)
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1  Project Brief.mp4 (16.25 MB)
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2  Solution Walkthrough.mp4 (55.88 MB)
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1  What is Data Science.mp4 (12.27 MB)
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2  The Data Science Skillset.mp4 (7.28 MB)
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3  What is Machine Learning.mp4 (13.95 MB)
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4  Common Machine Learning Algorithms.mp4 (10.34 MB)
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5  Data Science Workflow.mp4 (5.54 MB)
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6  Data Prep & EDA Steps.mp4 (20 MB)
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7  Modeling Steps.mp4 (17.16 MB)
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8  Classification Modeling.mp4 (3.57 MB)
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9  Key Takeaways.mp4 (7.58 MB)
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1  Classification 101.mp4 (29.24 MB)
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2  Goals of Classification.mp4 (10.45 MB)
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3  Types of Classification.mp4 (10.53 MB)
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4  Classification Modeling Workflow.mp4 (13.92 MB)
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5  Key Takeaways.mp4 (7.87 MB)
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1  EDA For Classification.mp4 (18.15 MB)
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10  PRO TIP Correlation Matrix.mp4 (12.57 MB)
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11  DEMO Correlation Matrix.mp4 (31.34 MB)
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12  Feature-Target Relationships.mp4 (37.09 MB)
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13  Feature-Feature Relationships.mp4 (13.91 MB)
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14  PRO TIP Pair Plots.mp4 (43.73 MB)
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15  ASSIGNMENT Exploring Relationships.mp4 (11.66 MB)
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16  SOLUTION Exploring Relationships.mp4 (51.22 MB)
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17  Feature Engineering Overview.mp4 (24.06 MB)
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18  Numeric Feature Engineering.mp4 (22.87 MB)
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19  Dummy Variables.mp4 (24.54 MB)
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2  Defining a Target.mp4 (21.9 MB)
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20  Binning Categories.mp4 (19.29 MB)
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21  DEMO Feature Engineering.mp4 (44.63 MB)
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22  Data Splitting.mp4 (31.68 MB)
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23  Preparing Data for Modeling.mp4 (9.47 MB)
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24  ASSIGNMENT Preparing the Data for Modeling.mp4 (7.55 MB)
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25  SOLUTION Prepare the Data for Modeling.mp4 (57.3 MB)
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26  Key Takeaways.mp4 (7.28 MB)
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3  DEMO Defining a Target.mp4 (36.24 MB)
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4  Exploring the Target.mp4 (19.65 MB)
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5  Exploring the Features.mp4 (10.39 MB)
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6  DEMO Exploring the Features.mp4 (28.35 MB)
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7  ASSIGNMENT Exploring the Target & Features.mp4 (14.16 MB)
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8  SOLUTION Exploring the Target & Features.mp4 (39.29 MB)
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9  Correlation.mp4 (21.7 MB)
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1  K-Nearest Neighbors.mp4 (30.63 MB)
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10  Overfitting & Validation.mp4 (37.35 MB)
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11  DEMO Hyperparameter Tuning.mp4 (29.67 MB)
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12  Hard vs  Soft Classification.mp4 (23.36 MB)
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13  DEMO Probability vs  Event Rate.mp4 (59.04 MB)
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14  ASSIGNMENT Tuning a KNN Model.mp4 (7.33 MB)
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15  SOLUTION Tuning a KNN Model.mp4 (24.76 MB)
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16  Pros & Cons of KNN.mp4 (25.96 MB)
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17  Key Takeaways.mp4 (6.08 MB)
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2  The KNN Workflow.mp4 (27.29 MB)
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3  KNN in Python.mp4 (11.87 MB)
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4  Model Accuracy.mp4 (20.78 MB)
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5  Confusion Matrix.mp4 (20.09 MB)
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6  DEMO Confusion Matrix.mp4 (23.28 MB)
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7  ASSIGNMENT Fitting a Simple KNN Model.mp4 (10.84 MB)
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8  SOLUTION Fitting a Simple KNN Model.mp4 (28.13 MB)
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9  Hyperparameter Tuning.mp4 (16.36 MB)
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1  Logistic Regression.mp4 (15.26 MB)
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10  SOLUTION Logistic Regression.mp4 (24.45 MB)
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11  Feature Engineering & Selection.mp4 (21.69 MB)
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12  Regularization.mp4 (24.41 MB)
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13  Tuning a Regularized Model.mp4 (21.43 MB)
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14  DEMO Regularized Logistic Regression.mp4 (31.28 MB)
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15  ASSIGNMENT Regularized Logistic Regression.mp4 (6.87 MB)
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16  SOLUTION Regularized Logistic Regression.mp4 (43.56 MB)
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17  Multi-class Logistic Regression.mp4 (34.54 MB)
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18  ASSIGNMENT Multi-class Logistic Regression.mp4 (8.19 MB)
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19  SOLUTION Multi-class Logistic Regression.mp4 (19.88 MB)
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2  Logistic vs  Linear Regression.mp4 (11.99 MB)
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20  Pros & Cons of Logistic Regression.mp4 (16.28 MB)
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21  Key Takeaways.mp4 (7.95 MB)
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3  The Logistic Function.mp4 (14.23 MB)
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4  Likelihood.mp4 (23.36 MB)
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5  Multiple Logistic Regression.mp4 (14.14 MB)
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6  The Logistic Regression Workflow.mp4 (4.41 MB)
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7  Logistic Regression in Python.mp4 (27.9 MB)
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8  Interpreting Coefficients.mp4 (21.08 MB)
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9  ASSIGNMENT Logistic Regression.mp4 (9.5 MB)
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1  Classification Metrics.mp4 (13.55 MB)
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10  DEMO Plotting Precision-Recall & F1 Curves.mp4 (23.48 MB)
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11  The ROC Curve & AUC.mp4 (19.02 MB)
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12  DEMO The ROC Curve & AUC.mp4 (19.83 MB)
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13  Classification Metrics Recap.mp4 (14.82 MB)
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14  ASSIGNMENT Threshold Shifting.mp4 (7.01 MB)
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15  SOLUTION Threshold Shifting.mp4 (36.73 MB)
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16  Multi-class Metrics.mp4 (29.77 MB)
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17  Multi-class Metrics in Python.mp4 (8.48 MB)
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18  ASSIGNMENT Multi-class Metrics.mp4 (6.24 MB)
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19  SOLUTION Multi-class Metrics.mp4 (16.62 MB)
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2  Accuracy, Precision & Recall.mp4 (29.58 MB)
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20  Key Takeaways.mp4 (9.89 MB)
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3  DEMO Accuracy, Precision & Recall.mp4 (33.87 MB)
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4  PRO TIP F1 Score.mp4 (18.68 MB)
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5  ASSIGNMENT Model Metrics.mp4 (5.29 MB)
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6  SOLUTION Model Metrics.mp4 (24.32 MB)
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7  Soft Classification.mp4 (31.1 MB)
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8  DEMO Leveraging Soft Classification.mp4 (19.75 MB)
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9  PRO TIP Precision-Recall & F1 Curves.mp4 (20.92 MB)
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1  Imbalanced Data.mp4 (23.47 MB)
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10  Undersampling.mp4 (9.84 MB)
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11  Undersampling in Python.mp4 (33.41 MB)
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12  ASSIGNMENT Sampling Methods.mp4 (16.99 MB)
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13  SOLUTION Sampling Methods.mp4 (45.33 MB)
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14  Changing Class Weights.mp4 (16.56 MB)
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15  DEMO Changing Class Weights.mp4 (21.35 MB)
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16  ASSIGNMENT Changing Class Weights.mp4 (4.27 MB)
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17  SOLUTION Changing Class Weights.mp4 (21.91 MB)
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18  Imbalanced Data Recap.mp4 (8.28 MB)
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19  Key Takeaways.mp4 (5.55 MB)
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2  Managing Imbalanced Data.mp4 (21.26 MB)
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3  Threshold Shifting.mp4 (11.26 MB)
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4  Sampling Strategies.mp4 (10.64 MB)
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5  Oversampling.mp4 (7.25 MB)
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6  Oversampling in Python.mp4 (15.4 MB)
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7  DEMO Oversampling.mp4 (28.13 MB)
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8  SMOTE.mp4 (5.67 MB)
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9  SMOTE in Python.mp4 (14.87 MB)
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1  Project Brief.mp4 (24.21 MB)
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2  Solution Walkthrough.mp4 (75.86 MB)
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