Udemy - Python Data Science Classification Modeling (11.2024)

Creation Time	Aug. 2, 2025, 10:04 p.m.
Last Access Time	March 19, 2026, 7:17 a.m.
File Size	2.2 GB
Keywords	11 Classification Python Science - Udemy 2024 Modeling Data
Total Requests	678
Total Files	338
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01 - Introduction/005 Data-Science-in-Python-Classification.zip 98.4 MB
12 - Classification Summary/003 DEMO Production Pipeline & Deployment.mp4 55.7 MB
09 - Mid-Course Project/002 Solution Walkthrough.mp4 53.8 MB
04 - Data Prep & EDA/025 SOLUTION Prepare the Data for Modeling.mp4 49.2 MB
11 - Ensemble Models/012 SOLUTION Random Forests.mp4 43.6 MB
06 - Logistic Regression/016 SOLUTION Regularized Logistic Regression.mp4 43.6 MB
13 - Final Project/002 Solution Walkthrough.mp4 38.6 MB
04 - Data Prep & EDA/014 PRO TIP Pair Plots.mp4 37.1 MB
04 - Data Prep & EDA/003 DEMO Defining a Target.mp4 36.2 MB
01 - Introduction/005 Data-Science-in-Python-Classification.pdf 34.4 MB
05 - K-Nearest Neighbors/013 DEMO Probability vs. Event Rate.mp4 33.6 MB
10 - Decision Trees/005 DEMO Decision Trees.mp4 32.3 MB
04 - Data Prep & EDA/022 Data Splitting.mp4 31.7 MB
04 - Data Prep & EDA/011 DEMO Correlation Matrix.mp4 31.3 MB
05 - K-Nearest Neighbors/001 K-Nearest Neighbors.mp4 30.6 MB
10 - Decision Trees/008 SOLUTION Decision Trees.mp4 29.9 MB
01 - Introduction/001 Course Introduction.mp4 29.3 MB
11 - Ensemble Models/009 PRO TIP Random Search.mp4 26.5 MB
04 - Data Prep & EDA/016 SOLUTION Exploring Relationships.mp4 26.5 MB
10 - Decision Trees/009 Hyperparameter Tuning for Decision Trees.mp4 26.2 MB
05 - K-Nearest Neighbors/008 SOLUTION Fitting a Simple KNN Model.mp4 24.5 MB
04 - Data Prep & EDA/021 DEMO Feature Engineering.mp4 24.4 MB
01 - Introduction/008 Jupyter Installation & Launch.mp4 23.8 MB
07 - Classification Metrics/010 DEMO Plotting Precision-Recall & F1 Curves.mp4 23.5 MB
05 - K-Nearest Neighbors/006 DEMO Confusion Matrix.mp4 23.3 MB
11 - Ensemble Models/016 DEMO Hyperparameter Tuning for Gradient Boosting.mp4 22.9 MB
06 - Logistic Regression/014 DEMO Regularized Logistic Regression.mp4 22.9 MB
04 - Data Prep & EDA/008 SOLUTION Exploring the Target & Features.mp4 22.5 MB
08 - Imbalanced Data/002 Managing Imbalanced Data.mp4 21.2 MB
04 - Data Prep & EDA/012 Feature-Target Relationships.mp4 21.2 MB
05 - K-Nearest Neighbors/002 The KNN Workflow.mp4 20.6 MB
05 - K-Nearest Neighbors/005 Confusion Matrix.mp4 20.1 MB
05 - K-Nearest Neighbors/010 Overfitting & Validation.mp4 19.3 MB
07 - Classification Metrics/003 DEMO Accuracy, Precision & Recall.mp4 19.0 MB
05 - K-Nearest Neighbors/015 SOLUTION Tuning a KNN Model.mp4 18.4 MB
08 - Imbalanced Data/013 SOLUTION Sampling Methods.mp4 18.2 MB
07 - Classification Metrics/015 SOLUTION Threshold Shifting.mp4 16.6 MB
11 - Ensemble Models/020 PRO TIP SHAP Values.mp4 16.6 MB
05 - K-Nearest Neighbors/004 Model Accuracy.mp4 16.1 MB
05 - K-Nearest Neighbors/011 DEMO Hyperparameter Tuning.mp4 15.7 MB
06 - Logistic Regression/017 Multi-class Logistic Regression.mp4 15.5 MB
08 - Imbalanced Data/011 Undersampling in Python.mp4 15.5 MB
06 - Logistic Regression/001 Logistic Regression.mp4 15.3 MB
06 - Logistic Regression/007 Logistic Regression in Python.mp4 15.2 MB
10 - Decision Trees/006 Feature Importance.mp4 15.0 MB
11 - Ensemble Models/019 SOLUTION Gradient Boosting.mp4 14.9 MB
10 - Decision Trees/010 DEMO Hyperparameter Tuning.mp4 14.9 MB
11 - Ensemble Models/021 DEMO SHAP Values.mp4 14.7 MB
10 - Decision Trees/012 SOLUTION Tuned Decision Tree.mp4 14.7 MB
09 - Mid-Course Project/001 Project Brief.mp4 14.2 MB
07 - Classification Metrics/007 Soft Classification.mp4 14.2 MB
05 - K-Nearest Neighbors/012 Hard vs. Soft Classification.mp4 14.1 MB
13 - Final Project/001 Project Brief.mp4 14.1 MB
11 - Ensemble Models/008 Hyperparameter Tuning for Random Forests.mp4 13.5 MB
11 - Ensemble Models/003 DEMO Simple Ensemble Models.mp4 13.5 MB
07 - Classification Metrics/002 Accuracy, Precision & Recall.mp4 13.3 MB
07 - Classification Metrics/016 Multi-class Metrics.mp4 12.6 MB
08 - Imbalanced Data/012 ASSIGNMENT Sampling Methods.mp4 12.6 MB
03 - Classification 101/001 Classification 101.mp4 12.3 MB
04 - Data Prep & EDA/017 Feature Engineering Overview.mp4 12.3 MB
04 - Data Prep & EDA/004 Exploring the Target.mp4 12.0 MB
06 - Logistic Regression/002 Logistic vs. Linear Regression.mp4 12.0 MB
01 - Introduction/003 Course Structure & Outline.mp4 11.8 MB
11 - Ensemble Models/015 Hyperparameter Tuning for Gradient Boosting.mp4 11.6 MB
08 - Imbalanced Data/007 DEMO Oversampling.mp4 11.6 MB
10 - Decision Trees/002 Entropy.mp4 11.5 MB
06 - Logistic Regression/012 Regularization.mp4 11.3 MB
11 - Ensemble Models/005 SOLUTION Simple Ensemble Models.mp4 11.2 MB
11 - Ensemble Models/007 Fitting Random Forests in Python.mp4 11.2 MB
04 - Data Prep & EDA/006 DEMO Exploring the Features.mp4 11.0 MB
07 - Classification Metrics/006 SOLUTION Model Metrics.mp4 10.9 MB
06 - Logistic Regression/011 Feature Engineering & Selection.mp4 10.9 MB
05 - K-Nearest Neighbors/016 Pros & Cons of KNN.mp4 10.8 MB
05 - K-Nearest Neighbors/007 ASSIGNMENT Fitting a Simple KNN Model.mp4 10.8 MB
04 - Data Prep & EDA/019 Dummy Variables.mp4 10.7 MB
06 - Logistic Regression/004 Likelihood.mp4 10.6 MB
03 - Classification 101/003 Types of Classification.mp4 10.5 MB
08 - Imbalanced Data/017 SOLUTION Changing Class Weights.mp4 10.5 MB
04 - Data Prep & EDA/009 Correlation.mp4 10.3 MB
07 - Classification Metrics/012 DEMO The ROC Curve & AUC.mp4 10.1 MB
01 - Introduction/007 Setting Expectations.mp4 10.1 MB
04 - Data Prep & EDA/018 Numeric Feature Engineering.mp4 9.8 MB
07 - Classification Metrics/004 PRO TIP F1 Score.mp4 9.7 MB
04 - Data Prep & EDA/002 Defining a Target.mp4 9.7 MB
11 - Ensemble Models/013 Gradient Boosting.mp4 9.6 MB
07 - Classification Metrics/019 SOLUTION Multi-class Metrics.mp4 9.6 MB
08 - Imbalanced Data/001 Imbalanced Data.mp4 9.5 MB
06 - Logistic Regression/010 SOLUTION Logistic Regression.mp4 9.4 MB
10 - Decision Trees/003 Decision Tree Predictions.mp4 9.4 MB
05 - K-Nearest Neighbors/003 KNN in Python.mp4 9.4 MB
06 - Logistic Regression/019 SOLUTION Multi-class Logistic Regression.mp4 9.2 MB
08 - Imbalanced Data/015 DEMO Changing Class Weights.mp4 9.2 MB
06 - Logistic Regression/008 Interpreting Coefficients.mp4 9.0 MB
06 - Logistic Regression/013 Tuning a Regularized Model.mp4 8.9 MB
07 - Classification Metrics/008 DEMO Leveraging Soft Classification.mp4 8.8 MB
07 - Classification Metrics/009 PRO TIP Precision-Recall & F1 Curves.mp4 8.8 MB
11 - Ensemble Models/001 Ensemble Models.mp4 8.7 MB
02 - Intro to Data Science/006 Data Prep & EDA Steps.mp4 8.4 MB
12 - Classification Summary/002 Pros & Cons of Classification Models.mp4 8.4 MB
07 - Classification Metrics/013 Classification Metrics Recap.mp4 8.3 MB
04 - Data Prep & EDA/020 Binning Categories.mp4 8.1 MB
04 - Data Prep & EDA/007 ASSIGNMENT Exploring the Target & Features.mp4 8.0 MB
07 - Classification Metrics/011 The ROC Curve & AUC.mp4 7.8 MB
04 - Data Prep & EDA/001 EDA For Classification.mp4 7.6 MB
10 - Decision Trees/001 Decision Trees.mp4 7.5 MB
06 - Logistic Regression/009 ASSIGNMENT Logistic Regression.mp4 7.5 MB
05 - K-Nearest Neighbors/009 Hyperparameter Tuning.mp4 7.3 MB
12 - Classification Summary/001 Recap Classification Models & Workflow.mp4 7.3 MB
02 - Intro to Data Science/007 Modeling Steps.mp4 7.1 MB
08 - Imbalanced Data/014 Changing Class Weights.mp4 6.9 MB
10 - Decision Trees/004 Decision Trees in Python.mp4 6.7 MB
06 - Logistic Regression/003 The Logistic Function.mp4 6.6 MB
10 - Decision Trees/013 Pros & Cons of Decision Trees.mp4 6.6 MB
06 - Logistic Regression/020 Pros & Cons of Logistic Regression.mp4 6.6 MB
03 - Classification 101/004 Classification Modeling Workflow.mp4 6.5 MB
06 - Logistic Regression/005 Multiple Logistic Regression.mp4 6.5 MB
08 - Imbalanced Data/006 Oversampling in Python.mp4 6.5 MB
11 - Ensemble Models/006 Random Forests.mp4 6.3 MB
11 - Ensemble Models/014 Gradient Boosting in Python.mp4 6.2 MB
08 - Imbalanced Data/009 SMOTE in Python.mp4 6.1 MB
02 - Intro to Data Science/003 What is Machine Learning.mp4 6.1 MB
04 - Data Prep & EDA/013 Feature-Feature Relationships.mp4 5.9 MB
04 - Data Prep & EDA/010 PRO TIP Correlation Matrix.mp4 5.6 MB
12 - Classification Summary/004 Looking Ahead Unsupervised Learning.mp4 5.5 MB
07 - Classification Metrics/001 Classification Metrics.mp4 5.5 MB
08 - Imbalanced Data/003 Threshold Shifting.mp4 5.3 MB
02 - Intro to Data Science/001 What is Data Science.mp4 5.2 MB
11 - Ensemble Models/002 Simple Ensemble Models.mp4 5.2 MB
06 - Logistic Regression/015 ASSIGNMENT Regularized Logistic Regression.mp4 5.1 MB
04 - Data Prep & EDA/015 ASSIGNMENT Exploring Relationships.mp4 5.1 MB
04 - Data Prep & EDA/005 Exploring the Features.mp4 4.7 MB
08 - Imbalanced Data/010 Undersampling.mp4 4.7 MB
01 - Introduction/006 Introducing the Course Project.mp4 4.5 MB
11 - Ensemble Models/004 ASSIGNMENT Simple Ensemble Models.mp4 4.4 MB
08 - Imbalanced Data/004 Sampling Strategies.mp4 4.3 MB
02 - Intro to Data Science/004 Common Machine Learning Algorithms.mp4 4.3 MB
04 - Data Prep & EDA/023 Preparing Data for Modeling.mp4 4.3 MB
03 - Classification 101/002 Goals of Classification.mp4 4.3 MB
11 - Ensemble Models/017 Pros & Cons of Gradient Boosting.mp4 4.2 MB
06 - Logistic Regression/018 ASSIGNMENT Multi-class Logistic Regression.mp4 4.2 MB
11 - Ensemble Models/010 Pros & Cons of Random Forests.mp4 4.1 MB
04 - Data Prep & EDA/024 ASSIGNMENT Preparing the Data for Modeling.mp4 4.1 MB
07 - Classification Metrics/014 ASSIGNMENT Threshold Shifting.mp4 4.1 MB
05 - K-Nearest Neighbors/014 ASSIGNMENT Tuning a KNN Model.mp4 3.9 MB
07 - Classification Metrics/020 Key Takeaways.mp4 3.7 MB
07 - Classification Metrics/017 Multi-class Metrics in Python.mp4 3.6 MB
01 - Introduction/002 About This Series.mp4 3.6 MB
02 - Intro to Data Science/008 Classification Modeling.mp4 3.6 MB
08 - Imbalanced Data/018 Imbalanced Data Recap.mp4 3.4 MB
10 - Decision Trees/007 ASSIGNMENT Decision Trees.mp4 3.4 MB
02 - Intro to Data Science/002 The Data Science Skillset.mp4 3.4 MB
08 - Imbalanced Data/005 Oversampling.mp4 3.3 MB
06 - Logistic Regression/021 Key Takeaways.mp4 3.2 MB
03 - Classification 101/005 Key Takeaways.mp4 3.0 MB
07 - Classification Metrics/005 ASSIGNMENT Model Metrics.mp4 3.0 MB
02 - Intro to Data Science/009 Key Takeaways.mp4 2.9 MB
04 - Data Prep & EDA/026 Key Takeaways.mp4 2.9 MB
07 - Classification Metrics/018 ASSIGNMENT Multi-class Metrics.mp4 2.9 MB
08 - Imbalanced Data/008 SMOTE.mp4 2.5 MB
11 - Ensemble Models/022 Key Takeaways.mp4 2.4 MB
10 - Decision Trees/014 Key Takeaways.mp4 2.4 MB
05 - K-Nearest Neighbors/017 Key Takeaways.mp4 2.4 MB
02 - Intro to Data Science/005 Data Science Workflow.mp4 2.4 MB
11 - Ensemble Models/011 ASSIGNMENT Random Forests.mp4 2.3 MB
08 - Imbalanced Data/016 ASSIGNMENT Changing Class Weights.mp4 2.3 MB
11 - Ensemble Models/018 ASSIGNMENT Gradient Boosting.mp4 2.3 MB
08 - Imbalanced Data/019 Key Takeaways.mp4 2.2 MB
10 - Decision Trees/011 ASSIGNMENT Tuned Decision Tree.mp4 2.0 MB
06 - Logistic Regression/006 The Logistic Regression Workflow.mp4 1.9 MB
09 - Mid-Course Project/002 Solution Walkthrough.srt 19.0 KB
12 - Classification Summary/003 DEMO Production Pipeline & Deployment.srt 18.8 KB
05 - K-Nearest Neighbors/013 DEMO Probability vs. Event Rate.srt 16.4 KB
04 - Data Prep & EDA/016 SOLUTION Exploring Relationships.srt 14.0 KB
04 - Data Prep & EDA/008 SOLUTION Exploring the Target & Features.srt 13.8 KB
04 - Data Prep & EDA/025 SOLUTION Prepare the Data for Modeling.srt 12.6 KB
14 - Next Steps/001 BONUS LESSON.html 12.1 KB
04 - Data Prep & EDA/012 Feature-Target Relationships.srt 12.0 KB
05 - K-Nearest Neighbors/010 Overfitting & Validation.srt 11.8 KB
04 - Data Prep & EDA/021 DEMO Feature Engineering.srt 11.7 KB
07 - Classification Metrics/007 Soft Classification.srt 11.4 KB
06 - Logistic Regression/017 Multi-class Logistic Regression.srt 11.4 KB
13 - Final Project/002 Solution Walkthrough.srt 11.1 KB
07 - Classification Metrics/002 Accuracy, Precision & Recall.srt 10.8 KB
05 - K-Nearest Neighbors/011 DEMO Hyperparameter Tuning.srt 10.0 KB
10 - Decision Trees/008 SOLUTION Decision Trees.srt 10.0 KB
11 - Ensemble Models/020 PRO TIP SHAP Values.srt 10.0 KB
05 - K-Nearest Neighbors/001 K-Nearest Neighbors.srt 9.8 KB
07 - Classification Metrics/015 SOLUTION Threshold Shifting.srt 9.5 KB
03 - Classification 101/001 Classification 101.srt 9.4 KB
04 - Data Prep & EDA/003 DEMO Defining a Target.srt 9.3 KB
10 - Decision Trees/002 Entropy.srt 9.3 KB
06 - Logistic Regression/012 Regularization.srt 9.2 KB
07 - Classification Metrics/016 Multi-class Metrics.srt 9.2 KB
11 - Ensemble Models/012 SOLUTION Random Forests.srt 8.8 KB
04 - Data Prep & EDA/022 Data Splitting.srt 8.8 KB
07 - Classification Metrics/003 DEMO Accuracy, Precision & Recall.srt 8.7 KB
11 - Ensemble Models/009 PRO TIP Random Search.srt 8.6 KB
04 - Data Prep & EDA/006 DEMO Exploring the Features.srt 8.6 KB
04 - Data Prep & EDA/009 Correlation.srt 8.6 KB
08 - Imbalanced Data/011 Undersampling in Python.srt 8.5 KB
08 - Imbalanced Data/013 SOLUTION Sampling Methods.srt 8.5 KB
10 - Decision Trees/006 Feature Importance.srt 8.5 KB
04 - Data Prep & EDA/017 Feature Engineering Overview.srt 8.5 KB
11 - Ensemble Models/021 DEMO SHAP Values.srt 8.4 KB
05 - K-Nearest Neighbors/002 The KNN Workflow.srt 8.3 KB
09 - Mid-Course Project/001 Project Brief.srt 8.3 KB
05 - K-Nearest Neighbors/012 Hard vs. Soft Classification.srt 8.1 KB
11 - Ensemble Models/015 Hyperparameter Tuning for Gradient Boosting.srt 8.1 KB
06 - Logistic Regression/004 Likelihood.srt 8.0 KB
04 - Data Prep & EDA/011 DEMO Correlation Matrix.srt 7.9 KB
11 - Ensemble Models/008 Hyperparameter Tuning for Random Forests.srt 7.9 KB
04 - Data Prep & EDA/019 Dummy Variables.srt 7.7 KB
06 - Logistic Regression/007 Logistic Regression in Python.srt 7.7 KB
04 - Data Prep & EDA/002 Defining a Target.srt 7.6 KB
10 - Decision Trees/009 Hyperparameter Tuning for Decision Trees.srt 7.5 KB
08 - Imbalanced Data/007 DEMO Oversampling.srt 7.5 KB
04 - Data Prep & EDA/014 PRO TIP Pair Plots.srt 7.4 KB
04 - Data Prep & EDA/018 Numeric Feature Engineering.srt 7.4 KB
10 - Decision Trees/003 Decision Tree Predictions.srt 7.2 KB
04 - Data Prep & EDA/004 Exploring the Target.srt 7.2 KB
06 - Logistic Regression/016 SOLUTION Regularized Logistic Regression.srt 7.2 KB
05 - K-Nearest Neighbors/005 Confusion Matrix.srt 7.1 KB
01 - Introduction/008 Jupyter Installation & Launch.srt 7.0 KB
05 - K-Nearest Neighbors/016 Pros & Cons of KNN.srt 7.0 KB
07 - Classification Metrics/010 DEMO Plotting Precision-Recall & F1 Curves.srt 6.9 KB
10 - Decision Trees/012 SOLUTION Tuned Decision Tree.srt 6.9 KB
08 - Imbalanced Data/002 Managing Imbalanced Data.srt 6.8 KB
11 - Ensemble Models/007 Fitting Random Forests in Python.srt 6.8 KB
11 - Ensemble Models/019 SOLUTION Gradient Boosting.srt 6.8 KB
07 - Classification Metrics/006 SOLUTION Model Metrics.srt 6.7 KB
05 - K-Nearest Neighbors/006 DEMO Confusion Matrix.srt 6.5 KB
08 - Imbalanced Data/001 Imbalanced Data.srt 6.5 KB
11 - Ensemble Models/001 Ensemble Models.srt 6.5 KB
05 - K-Nearest Neighbors/008 SOLUTION Fitting a Simple KNN Model.srt 6.5 KB
05 - K-Nearest Neighbors/004 Model Accuracy.srt 6.5 KB
10 - Decision Trees/001 Decision Trees.srt 6.4 KB
06 - Logistic Regression/011 Feature Engineering & Selection.srt 6.4 KB
02 - Intro to Data Science/006 Data Prep & EDA Steps.srt 6.3 KB
06 - Logistic Regression/013 Tuning a Regularized Model.srt 6.3 KB
10 - Decision Trees/005 DEMO Decision Trees.srt 6.2 KB
07 - Classification Metrics/009 PRO TIP Precision-Recall & F1 Curves.srt 6.2 KB
07 - Classification Metrics/012 DEMO The ROC Curve & AUC.srt 6.1 KB
06 - Logistic Regression/014 DEMO Regularized Logistic Regression.srt 6.1 KB
05 - K-Nearest Neighbors/009 Hyperparameter Tuning.srt 6.1 KB
06 - Logistic Regression/019 SOLUTION Multi-class Logistic Regression.srt 6.0 KB
04 - Data Prep & EDA/001 EDA For Classification.srt 6.0 KB
07 - Classification Metrics/004 PRO TIP F1 Score.srt 6.0 KB
04 - Data Prep & EDA/020 Binning Categories.srt 5.7 KB
11 - Ensemble Models/003 DEMO Simple Ensemble Models.srt 5.7 KB
06 - Logistic Regression/008 Interpreting Coefficients.srt 5.6 KB
06 - Logistic Regression/010 SOLUTION Logistic Regression.srt 5.6 KB
11 - Ensemble Models/016 DEMO Hyperparameter Tuning for Gradient Boosting.srt 5.6 KB
08 - Imbalanced Data/017 SOLUTION Changing Class Weights.srt 5.6 KB
05 - K-Nearest Neighbors/015 SOLUTION Tuning a KNN Model.srt 5.4 KB
06 - Logistic Regression/005 Multiple Logistic Regression.srt 5.3 KB
07 - Classification Metrics/008 DEMO Leveraging Soft Classification.srt 5.3 KB
06 - Logistic Regression/003 The Logistic Function.srt 5.3 KB
11 - Ensemble Models/005 SOLUTION Simple Ensemble Models.srt 5.2 KB
12 - Classification Summary/002 Pros & Cons of Classification Models.srt 5.1 KB
10 - Decision Trees/004 Decision Trees in Python.srt 5.1 KB
02 - Intro to Data Science/007 Modeling Steps.srt 5.0 KB
07 - Classification Metrics/011 The ROC Curve & AUC.srt 4.9 KB
03 - Classification 101/004 Classification Modeling Workflow.srt 4.9 KB
06 - Logistic Regression/001 Logistic Regression.srt 4.8 KB
01 - Introduction/004 READ ME Important Notes for New Students.html 4.8 KB
12 - Classification Summary/001 Recap Classification Models & Workflow.srt 4.7 KB
02 - Intro to Data Science/001 What is Data Science.srt 4.7 KB
07 - Classification Metrics/019 SOLUTION Multi-class Metrics.srt 4.6 KB
13 - Final Project/001 Project Brief.srt 4.6 KB
08 - Imbalanced Data/015 DEMO Changing Class Weights.srt 4.6 KB
08 - Imbalanced Data/006 Oversampling in Python.srt 4.5 KB
02 - Intro to Data Science/003 What is Machine Learning.srt 4.4 KB
08 - Imbalanced Data/014 Changing Class Weights.srt 4.4 KB
10 - Decision Trees/013 Pros & Cons of Decision Trees.srt 4.4 KB
10 - Decision Trees/010 DEMO Hyperparameter Tuning.srt 4.4 KB
06 - Logistic Regression/020 Pros & Cons of Logistic Regression.srt 4.3 KB
07 - Classification Metrics/001 Classification Metrics.srt 4.3 KB
04 - Data Prep & EDA/013 Feature-Feature Relationships.srt 4.3 KB
08 - Imbalanced Data/009 SMOTE in Python.srt 4.2 KB
06 - Logistic Regression/002 Logistic vs. Linear Regression.srt 4.2 KB
04 - Data Prep & EDA/007 ASSIGNMENT Exploring the Target & Features.srt 4.1 KB
01 - Introduction/003 Course Structure & Outline.srt 4.1 KB
04 - Data Prep & EDA/010 PRO TIP Correlation Matrix.srt 4.0 KB
08 - Imbalanced Data/012 ASSIGNMENT Sampling Methods.srt 4.0 KB
07 - Classification Metrics/013 Classification Metrics Recap.srt 4.0 KB
04 - Data Prep & EDA/005 Exploring the Features.srt 3.9 KB
03 - Classification 101/003 Types of Classification.srt 3.9 KB
08 - Imbalanced Data/003 Threshold Shifting.srt 3.8 KB
04 - Data Prep & EDA/023 Preparing Data for Modeling.srt 3.8 KB
11 - Ensemble Models/013 Gradient Boosting.srt 3.8 KB
08 - Imbalanced Data/010 Undersampling.srt 3.7 KB
11 - Ensemble Models/002 Simple Ensemble Models.srt 3.7 KB
04 - Data Prep & EDA/024 ASSIGNMENT Preparing the Data for Modeling.srt 3.7 KB
11 - Ensemble Models/014 Gradient Boosting in Python.srt 3.6 KB
05 - K-Nearest Neighbors/003 KNN in Python.srt 3.5 KB
08 - Imbalanced Data/018 Imbalanced Data Recap.srt 3.4 KB
02 - Intro to Data Science/004 Common Machine Learning Algorithms.srt 3.3 KB
03 - Classification 101/002 Goals of Classification.srt 3.2 KB
01 - Introduction/001 Course Introduction.srt 3.1 KB
08 - Imbalanced Data/004 Sampling Strategies.srt 3.1 KB
05 - K-Nearest Neighbors/007 ASSIGNMENT Fitting a Simple KNN Model.srt 3.0 KB
02 - Intro to Data Science/002 The Data Science Skillset.srt 2.9 KB
11 - Ensemble Models/017 Pros & Cons of Gradient Boosting.srt 2.9 KB
06 - Logistic Regression/021 Key Takeaways.srt 2.9 KB
04 - Data Prep & EDA/026 Key Takeaways.srt 2.9 KB
04 - Data Prep & EDA/015 ASSIGNMENT Exploring Relationships.srt 2.8 KB
06 - Logistic Regression/009 ASSIGNMENT Logistic Regression.srt 2.8 KB
07 - Classification Metrics/017 Multi-class Metrics in Python.srt 2.7 KB
06 - Logistic Regression/018 ASSIGNMENT Multi-class Logistic Regression.srt 2.6 KB
08 - Imbalanced Data/005 Oversampling.srt 2.5 KB
11 - Ensemble Models/018 ASSIGNMENT Gradient Boosting.srt 2.5 KB
10 - Decision Trees/007 ASSIGNMENT Decision Trees.srt 2.5 KB
07 - Classification Metrics/020 Key Takeaways.srt 2.5 KB
01 - Introduction/007 Setting Expectations.srt 2.4 KB
07 - Classification Metrics/014 ASSIGNMENT Threshold Shifting.srt 2.4 KB
11 - Ensemble Models/004 ASSIGNMENT Simple Ensemble Models.srt 2.3 KB
05 - K-Nearest Neighbors/014 ASSIGNMENT Tuning a KNN Model.srt 2.3 KB
03 - Classification 101/005 Key Takeaways.srt 2.2 KB
11 - Ensemble Models/011 ASSIGNMENT Random Forests.srt 2.1 KB
02 - Intro to Data Science/009 Key Takeaways.srt 2.1 KB
08 - Imbalanced Data/008 SMOTE.srt 2.1 KB
08 - Imbalanced Data/016 ASSIGNMENT Changing Class Weights.srt 2.0 KB
05 - K-Nearest Neighbors/017 Key Takeaways.srt 2.0 KB
11 - Ensemble Models/022 Key Takeaways.srt 2.0 KB
06 - Logistic Regression/015 ASSIGNMENT Regularized Logistic Regression.srt 2.0 KB
11 - Ensemble Models/006 Random Forests.srt 2.0 KB
08 - Imbalanced Data/019 Key Takeaways.srt 2.0 KB
07 - Classification Metrics/005 ASSIGNMENT Model Metrics.srt 1.9 KB
02 - Intro to Data Science/005 Data Science Workflow.srt 1.9 KB
12 - Classification Summary/004 Looking Ahead Unsupervised Learning.srt 1.7 KB
10 - Decision Trees/014 Key Takeaways.srt 1.7 KB
07 - Classification Metrics/018 ASSIGNMENT Multi-class Metrics.srt 1.7 KB
10 - Decision Trees/011 ASSIGNMENT Tuned Decision Tree.srt 1.6 KB
06 - Logistic Regression/006 The Logistic Regression Workflow.srt 1.5 KB
01 - Introduction/006 Introducing the Course Project.srt 1.5 KB
01 - Introduction/002 About This Series.srt 1.1 KB
02 - Intro to Data Science/008 Classification Modeling.srt 1.1 KB
01 - Introduction/005 DOWNLOAD Course Resources.html 549 bytes

Udemy - Python Data Science Classification Modeling (11.2024)

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