Udemy - Imbalanced Classification Master Class in Python

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[ FreeCourseWeb.com ] Udemy - Imbalanced Classification Master Class in Python
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 01 Introduction
    • 001 Introduction.en.srt (1.3 KB)
    • 001 Introduction.mp4 (15.9 MB)
    • 002 Course Outcomes.en.srt (2.0 KB)
    • 002 Course Outcomes.mp4 (4.9 MB)
    • 003 Course Structure.en.srt (3.4 KB)
    • 003 Course Structure.mp4 (15.7 MB)
    • 004 Imbalanced Classification Defined.en.srt (6.0 KB)
    • 004 Imbalanced Classification Defined.mp4 (10.8 MB)
    • 005 Causes of Class Imbalance.en.srt (3.1 KB)
    • 005 Causes of Class Imbalance.mp4 (29.5 MB)
    • 006 Challenge of Imbalance Classification.en.srt (3.1 KB)
    • 006 Challenge of Imbalance Classification.mp4 (10.0 MB)
    • 007 Examples of Class Imbalance.en.srt (2.7 KB)
    • 007 Examples of Class Imbalance.mp4 (14.2 MB)
    02 Understanding Class Imbalance
    • 001 Create Synthetic Dataset with Class Distribution.en.srt (4.1 KB)
    • 001 Create Synthetic Dataset with Class Distribution.mp4 (4.1 MB)
    • 002 Effect of Skewed Class Distributions.en.srt (4.0 KB)
    • 002 Effect of Skewed Class Distributions.mp4 (11.2 MB)
    • 003 Visualizing Extreme Skew.en.srt (3.5 KB)
    • 003 Visualizing Extreme Skew.mp4 (7.2 MB)
    • 004 Why Imbalanced Classification Is Hard.en.srt (5.5 KB)
    • 004 Why Imbalanced Classification Is Hard.mp4 (18.6 MB)
    • 005 Compounding Effect of Dataset Size.en.srt (4.1 KB)
    • 005 Compounding Effect of Dataset Size.mp4 (12.7 MB)
    • 006 Compounding Effect of Label Noise.en.srt (5.0 KB)
    • 006 Compounding Effect of Label Noise.mp4 (13.6 MB)
    • 007 Compounding Effect of Data Distribution.en.srt (4.5 KB)
    • 007 Compounding Effect of Data Distribution.mp4 (11.8 MB)
    • 008 Imbalanced Classification in Python.ipynb (105.5 KB)
    03 Model Evaluation
    • 001 Evaluation Metrics and Imbalance.en.srt (5.1 KB)
    • 001 Evaluation Metrics and Imbalance.mp4 (38.9 MB)
    • 002 Taxonomy of Classifier Evaluation Metrics.en.srt (6.5 KB)
    • 002 Taxonomy of Classifier Evaluation Metrics.mp4 (9.0 MB)
    • 003 Ranking Metrics for Imbalanced Classification.en.srt (4.9 KB)
    • 003 Ranking Metrics for Imbalanced Classification.mp4 (8.2 MB)
    • 004 Probabilistic Metrics for Imbalanced Classification.en.srt (4.7 KB)
    • 004 Probabilistic Metrics for Imbalanced Classification.mp4 (21.4 MB)
    • 005 How to Choose an Evaluation Metric.en.srt (3.9 KB)
    • 005 How to Choose an Evaluation Metric.mp4 (7.1 MB)
    • 006 Accuracy Fails for Imbalanced Classification.en.srt (3.9 KB)
    • 006 Accuracy Fails for Imbalanced Classification.mp4 (24.9 MB)
    • 007 Accuracy Paradox.en.srt (2.9 KB)
    • 007 Accuracy Paradox.mp4 (18.8 MB)
    • 008 Demo_ Accuracy for Imbalanced Classification.en.srt (6.0 KB)
    • 008 Demo_ Accuracy for Imbalanced Classification.mp4 (21.3 MB)
    • 009 Precision for Imbalanced Classification.en.srt (4.3 KB)
    • 009 Precision for Imbalanced Classification.mp4 (11.0 MB)
    • 010 Precision for Multi-Class Classification.en.srt (4.0 KB)
    • 010 Precision for Multi-Class Classification.mp4 (16.4 MB)
    • 011 Recall for Imbalanced Classification.en.srt (3.5 KB)
    • 011 Recall for Imbalanced Classification.mp4 (10.9 MB)
    • 012 Demo_ Recall for Imbalanced Classification.en.srt (1.7 KB)
    • 012 Demo_ Recall for Imbalanced Classification.mp4 (6.7 MB)
    • 013 F-Measure for Imbalanced Classification.en.srt (4.4 KB)
    • 013 F-Measure for Imbalanced Classification.mp4 (9.5 MB)
    • 014 Demo_ F- Measure for Imbalanced Classification.en.srt (1.1 KB)
    • 014 Demo_ F- Measure for Imbalanced Classification.mp4 (3.1 MB)
    • 015 ROC Curves and Precision-Recall Curves.en.srt (3.3 KB)
    • 015 ROC Curves and Precision-Recall Curves.mp4 (7.8 MB)
    • 016 ROC Curve.en.srt (4.3 KB)
    • 016 ROC Curve.mp4 (6.8 MB)
    • 017 Demo_ ROC Curve.en.srt (3.1 KB)
    • 017 Demo_ ROC Curve.mp4 (10.3 MB)
    • 018 ROC Area Under Curve (AUC) Score.en.srt (3.1 KB)
    • 018 ROC Area Under Curve (AUC) Score.mp4 (10.3 MB)
    • 019 Precision-Recall Curves.en.srt (3.9 KB)
    • 019 Precision-Recall Curves.mp4 (12.2 MB)
    • 020 Precision-Recall Area Under Curve (AUC) Score.en.srt (1.8 KB)
    • 020 Precision-Recall Area Under Curve (AUC) Score.mp4 (7.8 MB)
    • 021 ROC AUC on with Severe Imbalance.en.srt (3.8 KB)
    • 021 ROC AUC on with Severe Imbalance.mp4 (13.5 MB)
    • 022 ROC and Precision-Recall Curves With a Severe Imbalance.en.srt (3.3 KB)
    • 022 ROC and Precision-Recall Curves With a Severe Imbalance.mp4 (9.4 MB)
    • 023 Probability Scoring Methods in Python.en.srt (2.4 KB)
    • 023 Probability Scoring Methods in Python.mp4 (5.9 MB)
    • 024 Log Loss Score.en.srt (3.9 KB)
    • 024 Log Loss Score.mp4 (11.5 MB)
    • 025 Brier Score.en.srt (5.2 KB)
    • 025 Brier Score.mp4 (15.0 MB)
    • 026 Cross-Validation for Imbalanced Classification.en.srt (1.9 KB)
    • 026 Cross-Validation for Imbalanced Classification.mp4 (14.9 MB)
    • 027 Challenge of Evaluating Classifiers.en.srt (3.8 KB)
    • 027 Challenge of Evaluating Classifiers.mp4 (8.0 MB)
    • 028 Failure of k-Fold Cross-Validation.en.srt (5.0 KB)
    • 028 Failure of k-Fold Cross-Validation.mp4 (17.5 MB)
    04 Data Sampling
    • 001 Data Sampling Methods for Imbalanced Classification.en.srt (2.4 KB)
    • 001 Data Sampling Methods for Imbalanced Classification.mp4 (9.6 MB)
    • 002 Oversampling Techniques.en.srt (3.1 KB)
    • 002 Oversampling Techniques.mp4 (8.5 MB)
    • 003 Undersampling Techniques.en.srt (6.0 KB)
    • 003 Undersampling Techniques.mp4 (12.1 MB)
    • 004 Combinations of Techniques.en.srt (2.4 KB)
    • 004 Combinations of Techniques.mp4 (4.4 MB)
    • 005 Random Resampling Imbalanced Datasets.en.srt (3.7 KB)
    • 005 Random Resampling Imbalanced Datasets.mp4 (11.9 MB)
    • 006 Demo_ Random Oversampling Imbalanced Datasets.en.srt (6.1 KB)
    • 006 Demo_ Random Oversampling Imbalanced Datasets.mp4 (22.4 MB)
    • 007 Demo_ Random Undersampling Imbalanced Datasets.en.srt (4.4 KB)
    • 007 Demo_ Random Undersampling Imbalanced Datasets.mp4 (18.4 MB)

Description

Imbalanced Classification Master Class in Python



MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 88 lectures (3h 6m) | Size: 861.2 MB
A Step-by-Step Guide to Handling Real-World Class Imbalance in Machine Learning
What you'll learn:
How to use data sampling algorithms like SMOTE to transform the training dataset for an imbalanced dataset when fitting a range of machine learning models
How algorithms from the field of cost-sensitive learning can be used for imbalanced classification
How to use modified versions of standard algorithms like SVM and decision trees to take the class weighting into account
How to tune the threshold when interpreting predicted probabilities as class labels
How to calibrate probabilities predicted by nonlinear algorithms that are not fit using a probabilistic framework
How to use algorithms from the field of outlier detection and anomaly detection for imbalanced classification
How to use modified ensemble algorithms that have been modified to take the class distribution into account during training
How to systematically work through an imbalanced classification predictive modeling project

Requirements
You'll need a solid foundation in machine learning
You'll need a solid background in Python
A familiarity with classification problems would be ideal

Description
Welcome to Imbalanced Classification Master Class in Python.

Classification predictive modeling is the task of assigning a label to an example. Imbalanced classification is those classification tasks where the distribution of examples across the classes is not equal. Typically the class distribution is severely skewed so that for each example in the minority class, there may be one hundred or even one thousand examples in the majority class. Practical imbalanced classification requires the use of a suite of specialized techniques, data preparation techniques, learning algorithms, and performance metrics.



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Udemy - Imbalanced Classification Master Class in Python

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Udemy - Imbalanced Classification Master Class in Python

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