Udemy - Machine Learning with Imbalanced Data

  • Category Other
  • Type Tutorials
  • Language English
  • Total size 3.0 GB
  • Uploaded By tutsnode
  • Downloads 246
  • Last checked 1 month ago
  • Date uploaded 5 years ago
  • Seeders 7
  • Leechers 5

Infohash : D8A03A5D9B9812EEA1A075B50C937CC98127D06F




Description

Welcome to Machine Learning with Imbalanced Datasets. In this course, you will learn multiple techniques which you can use with imbalanced datasets to improve the performance of your machine learning models.

If you are working with imbalanced datasets right now and want to improve the performance of your models, or you simply want to learn more about how to tackle data imbalance, this course will show you how.

We’ll take you step-by-step through engaging video tutorials and teach you everything you need to know about working with imbalanced datasets. Throughout this comprehensive course, we cover almost every available methodology to work with imbalanced datasets, discussing their logic, their implementation in Python, their advantages and shortcomings, and the considerations to have when using the technique. Specifically, you will learn:

Under-sampling methods at random or focused on highlighting certain sample populations
Over-sampling methods at random and those which create new examples based of existing observations
Ensemble methods that leverage the power of multiple weak learners in conjunction with sampling techniques to boost model performance
Cost sensitive methods which penalize wrong decisions more severely for minority classes
The appropriate metrics to evaluate model performance on imbalanced datasets

By the end of the course, you will be able to decide which technique is suitable for your dataset, and / or apply and compare the improvement in performance returned by the different methods on multiple datasets.

This comprehensive machine learning course includes over 50 lectures spanning about 8 hours of video, and ALL topics include hands-on Python code examples which you can use for reference and for practice, and re-use in your own projects.

In addition, the code is updated regularly to keep up with new trends and new Python library releases.

So what are you waiting for? Enroll today, learn how to work with imbalanced datasets and build better machine learning models.
Who this course is for:

Data Scientists and Machine Learning engineers working with imbalanced datasets

Requirements

Knowledge of machine learning basic algorithms, i.e., regression, decision trees and nearest neighbours
Python programming, including familiarity with NumPy, Pandas and Scikit-learn

Last Updated 1/2021

Files:

Machine Learning with Imbalanced Data [TutsNode.com] - Machine Learning with Imbalanced Data 3. Evaluation Metrics
  • 10. Geometric Mean, Dominance, Index of Imbalanced Accuracy - Demo.mp4 (86.8 MB)
  • 4. Precision, Recall and F-measure.srt (15.1 KB)
  • 16.1 Link to Jupyter notebook.html (0.2 KB)
  • 6. Precision, Recall and F-measure - Demo.mp4 (80.3 MB)
  • 10. Geometric Mean, Dominance, Index of Imbalanced Accuracy - Demo.srt (12.2 KB)
  • 6. Precision, Recall and F-measure - Demo.srt (12.2 KB)
  • 8. Confusion tables, FPR and FNR - Demo.srt (9.6 KB)
  • 13. Precision-Recall Curve.srt (9.2 KB)
  • 5. Install Yellowbrick.html (0.7 KB)
  • 11. ROC-AUC.srt (8.3 KB)
  • 7. Confusion tables, FPR and FNR.srt (7.4 KB)
  • 3. Accuracy - Demo.srt (7.3 KB)
  • 15. Additional reading resources (Optional).html (1.6 KB)
  • 16. Probability.srt (5.5 KB)
  • 12. ROC-AUC - Demo.srt (5.3 KB)
  • 2. Accuracy.srt (5.3 KB)
  • 9. Geometric Mean, Dominance, Index of Imbalanced Accuracy.srt (5.2 KB)
  • 14. Precision-Recall Curve - Demo.srt (3.4 KB)
  • 1. Introduction to Performance Metrics.srt (3.3 KB)
  • 4. Precision, Recall and F-measure.mp4 (67.0 MB)
  • 8. Confusion tables, FPR and FNR - Demo.mp4 (49.1 MB)
  • 3. Accuracy - Demo.mp4 (47.6 MB)
  • 13. Precision-Recall Curve.mp4 (40.5 MB)
  • 11. ROC-AUC.mp4 (39.3 MB)
  • 12. ROC-AUC - Demo.mp4 (31.6 MB)
  • 7. Confusion tables, FPR and FNR.mp4 (29.7 MB)
  • 9. Geometric Mean, Dominance, Index of Imbalanced Accuracy.mp4 (23.1 MB)
  • 2. Accuracy.mp4 (21.4 MB)
  • 16. Probability.mp4 (20.6 MB)
  • 14. Precision-Recall Curve - Demo.mp4 (18.1 MB)
  • 1. Introduction to Performance Metrics.mp4 (10.8 MB)
4. Udersampling
  • 23.1 Undersampling-Comparison.pdf (205.5 KB)
  • 3. Random Under-Sampling - Demo.srt (13.5 KB)
  • 22. Undersampling Method Comparison.srt (9.3 KB)
  • 5. Condensed Nearest Neighbours - Demo.srt (9.2 KB)
  • 4. Condensed Nearest Neighbours - Intro.srt (8.3 KB)
  • 2. Random Under-Sampling - Intro.srt (6.6 KB)
  • 1. Under-Sampling Methods - Introduction.srt (6.6 KB)
  • 12. Repeated Edited Nearest Neighbours - Intro.srt (5.4 KB)
  • 10. Edited Nearest Neighbours - Intro.srt (5.4 KB)
  • 8. One Sided Selection - Intro.srt (2.8 KB)
  • 6. Tomek Links - Intro.srt (5.3 KB)
  • 11. Edited Nearest Neighbours - Demo.srt (5.1 KB)
  • 16. Neighbourhood Cleaning Rule - Intro.srt (5.0 KB)
  • 20. Instance Hardness Threshold - Intro.srt (5.0 KB)
  • 21. Instance Hardness Threshold - Demo.srt (4.8 KB)
  • 9. One Sided Selection - Demo.srt (4.7 KB)
  • 19. NearMiss - Demo.srt (4.5 KB)
  • 18. NearMiss - Intro.srt (4.4 KB)
  • 17. Neighbourhood Cleaning Rule - Demo.srt (2.6 KB)
  • 14. All KNN - Intro.srt (4.3 KB)
  • 7. Tomek Links - Demo.srt (4.1 KB)
  • 13. Repeated Edited Nearest Neighbours - Demo.srt (3.9 KB)
  • 23. Summary Table.html (0.1 KB)
  • 15. All KNN - Demo.srt (3.6 KB)
  • 3. Random Under-Sampling - Demo.mp4 (66.9 MB)
  • 5. Condensed Nearest Neighbours - Demo.mp4 (52.7 MB)
  • 22. Undersampling Method Comparison.mp4 (47.5 MB)
  • 4. Condensed Nearest Neighbours - Intro.mp4 (32.4 MB)
  • 1. Under-Sampling Methods - Introduction.mp4 (31.5 MB)
  • 11. Edited Nearest Neighbours - Demo.mp4 (30.8 MB)
  • 21. Instance Hardness Threshold - Demo.mp4 (30.5 MB)
  • 19. NearMiss - Demo.mp4 (26.3 MB)
  • 2. Random Under-Sampling - Intro.mp4 (25.6 MB)
  • 9. One Sided Selection - Demo.mp4 (25.6 MB)
  • 12. Repeated Edited Nearest Neighbours - Intro.mp4 (24.3 MB)
  • 7. Tomek Links - Demo.mp4 (24.0 MB)
  • 16. Neighbourhood Cleaning Rule - Intro.mp4 (23.0 MB)
  • 13. Repeated Edited Nearest Neighbours - Demo.mp4 (22.9 MB)
  • 15. All KNN - Demo.mp4 (22.7 MB)
  • 10. Edited Nearest Neighbours - Intro.mp4 (22.6 MB)
  • 20. Instance Hardness Threshold - Intro.mp4 (19.7 MB)
  • 6. Tomek Links - Intro.mp4 (19.0 MB)
  • 18. NearMiss - Intro.mp4 (17.2 MB)
  • 14. All KNN - Intro.mp4 (16.3 MB)
  • 17. Neighbourhood Cleaning Rule - Demo.mp4 (15.9 MB)
  • 8. One Sided Selection - Intro.mp4 (11.9 MB)
8. Cost Sensitive Learning
  • 9. Bayes Conditional Risk.srt (14.7 KB)
  • 2. Types of Cost.srt (12.1 KB)
  • 7. Cost Sensitive Learning with Scikit-learn- Demo.srt (9.0 KB)
  • 10. MetaCost.srt (8.5 KB)
  • 1. Cost-sensitive Learning - Intro.srt (7.8 KB)
  • 12. Optional MetaCost Base Code.srt (7.5 KB)
  • 3. Obtaining the Cost.srt (4.6 KB)
  • 11. MetaCost - Demo.srt (4.5 KB)
  • 8. Find Optimal Cost with hyperparameter tuning.srt (4.4 KB)
  • 6. Misclassification Cost in Decision Trees.srt (4.1 KB)
  • 5. Misclassification Cost in Logistic Regression.srt (3.6 KB)
  • 13. Additional Reading Resources.html (2.0 KB)
  • 4. Cost Sensitive Approaches.srt (1.8 KB)
  • 9. Bayes Conditional Risk.mp4 (72.0 MB)
  • 7. Cost Sensitive Learning with Scikit-learn- Demo.mp4 (56.1 MB)
  • 2. Types of Cost.mp4 (44.0 MB)
  • 10. MetaCost.mp4 (42.6 MB)
  • 12. Optional MetaCost Base Code.mp4 (36.9 MB)
  • 1. Cost-sensitive Learning - Intro.mp4 (32.7 MB)
  • 11. MetaCost - Demo.mp4 (22.9 MB)
  • 8. Find Optimal Cost with hyperparameter tuning.mp4 (22.9 MB)
  • 6. Misclassification Cost in Decision Trees.mp4 (21.3 MB)
  • 3. Obtaining the Cost.mp4 (19.0 MB)
  • 5. Misclassification Cost in Logistic Regression.mp4 (18.7 MB)
  • 4. Cost Sensitive Approaches.mp4 (10.3 MB)
1. Introduction
  • 4. Code Jupyter notebooks.html (0.9 KB)
  • 5. Presentations covered in the course.html (0.3 KB)
  • 6. Python package Imbalanced-learn.html (0.7 KB)
  • 7. Download Datasets.html (0.3 KB)
  • 8. Additional resources for Machine Learning and Python programming.html (2.6 KB)
  • 3. Course Material.srt (2.4 KB)
  • 1. Introduction.srt (4.0 KB)
  • 2. Course Curriculum Overview.srt (3.9 KB)
  • 1. Introduction.mp4 (32.3 MB)
  • 2. Course Curriculum Overview.mp4 (17.5 MB)
  • 3. Course Material.mp4 (11.0 MB)
7. Ensemble Methods
  • 8. Ensemble Methods - Demo.srt (11.8 KB)
  • 5. Boosting.srt (10.6 KB)
  • 6. Boosting plus Re-Sampling.srt (8.0 KB)
  • 4. Bagging plus Over- or Under-Sampling.srt (6.4 KB)
  • 1. Ensemble methods with Imbalanced Data.srt (5.4 KB)
  • 7. Hybdrid Methods.srt (5.3 KB)
  • 3. Bagging.srt (3.2 KB)
  • 2. Foundations of Ensemble Learning.srt (3.2 KB)
  • 9. Additional Reading Resources.html (2.0 KB)
  • 8. Ensemble Methods - Demo.mp4 (70.8 MB)
  • 5. Boosting.mp4 (70.6 MB)
  • 6. Boosting plus Re-Sampling.mp4 (47.3 MB)
  • 4. Bagging plus Over- or Under-Sampling.mp4 (42.9 MB)
  • 7. Hybdrid Methods.mp4 (30.5 MB)
  • 1. Ensemble methods with Imbalanced Data.mp4 (26.5 MB)
  • 2. Foundations of Ensemble Learning.mp4 (19.7 MB)
  • 3. Bagging.mp4 (18.2 MB)
9. Probability Calibration
  • 3. Probability Calibration Curves - Demo.srt (11.5 KB)
  • 9. Calibrating a Classfiier after SMOTE or Under-sampling.srt (10.4 KB)
  • 5. Brier Score - Demo.srt (8.8 KB)
  • 1. Probability Calibration.srt (7.3 KB)
  • 8. Calibrating a Classifier - Demo.srt (7.3 KB)
  • 2. Probability Calibration Curves.srt (6.7 KB)
  • 6. Under- and Over-sampling and Cost-sensitive learning on Probability Calibration.srt (6.2 KB)
  • 7. Calibrating a Classifier.srt (5.9 KB)
  • 10. Calibrating a Classifier with Cost-sensitive Learning.srt (4.6 KB)
  • 4. Brier Score.srt (3.7 KB)
  • 11. Probability Additional reading resources.html (0.9 KB)
  • 3. Probability Calibration Curves - Demo.mp4 (64.9 MB)
  • 9. Calibrating a Classfiier after SMOTE or Under-sampling.mp4 (52.0 MB)
  • 5. Brier Score - Demo.mp4 (49.0 MB)
  • 8. Calibrating a Classifier - Demo.mp4 (46.7 MB)
  • 1. Probability Calibration.mp4 (34.1 MB)
  • 6. Under- and Over-sampling and Cost-sensitive learning on Probability Calibration.mp4 (29.6 MB)
  • 2. Probability Calibration Curves.mp4 (28.8 MB)
  • 7. Calibrating a Classifier.mp4 (27.2 MB)
  • 10. Calibrating a Classifier with Cost-sensitive Learning.mp4 (25.2 MB)
  • 4. Brier Score.mp4 (17.1 MB)
2. Machine Learning with Imbalanced Data Overview
  • 4. Additional Reading Resources (Optional).html (1.0 KB)
  • 1. Imbalanced classes - Introduction.srt (6.5 KB)
  • 2. Nature of the imbalanced class.srt (5.9 KB)
  • 3. Approaches to work with imbalanced datasets - Overview.srt (4.7 KB)
  • 2. Nature of the imbalanced class.mp4 (35.1 MB)
  • 1. Imbalanced classes - Introduction.mp4 (33.3 MB)
  • 3. Approaches to work with imbalanced datasets - Overview.mp4 (20.2 MB)
5. Oversampling
  • 6. SMOTE-NC.srt (10.4 KB)
  • 4. SMOTE.srt (10.0 KB)
  • 10. Borderline SMOTE.srt (9.3 KB)
  • 8. ADASYN.srt (7.7 KB)
  • 16. Over-Sampling Method Comparison.srt (7.2 KB)
  • 3. Random Over-Sampling - Demo.srt (6.3 KB)
  • 12. SVM SMOTE.srt (6.1 KB)
  • 14. K-Means SMOTE.srt (6.0 KB)
  • 13. SVM SMOTE - Demo.srt (4.9 KB)
  • 1. Over-Sampling Methods - Introduction.srt (4.4 KB)
  • 15. K-Means SMOTE - Demo.srt (3.9 KB)
  • 9. ADASYN - Demo.srt (3.7 KB)
  • 2. Random Over-Sampling.srt (3.7 KB)
  • 11. Borderline SMOTE - Demo.srt (3.6 KB)
  • 7. SMOTE-NC - Demo.srt (3.3 KB)
  • 5. SMOTE - Demo.srt (3.2 KB)
  • 6. SMOTE-NC.mp4 (48.0 MB)
  • 10. Borderline SMOTE.mp4 (46.2 MB)
  • 4. SMOTE.mp4 (44.6 MB)
  • 16. Over-Sampling Method Comparison.mp4 (39.8 MB)
  • 13. SVM SMOTE - Demo.mp4 (37.0 MB)
  • 3. Random Over-Sampling - Demo.mp4 (35.2 MB)
  • 8. ADASYN.mp4 (31.6 MB)
  • 14. K-Means SMOTE.mp4 (27.6 MB)
  • 12. SVM SMOTE.mp4 (25.3 MB)
  • 15. K-Means SMOTE - Demo.mp4 (24.8 MB)
  • 11. Borderline SMOTE - Demo.mp4 (24.8 MB)
  • 7. SMOTE-NC - Demo.mp4 (21.4 MB)
  • 1. Over-Sampling Methods - Introduction.mp4 (21.1 MB)
  • 9. ADASYN - Demo.mp4 (20.9 MB)
  • 5. SMOTE - Demo.mp4 (18.4 MB)
  • 2. Random Over-Sampling.mp4 (15.6 MB)
6. Over and Undersampling
  • 1. Combining Over and Under-sampling - Intro.srt (7.3 KB)
  • 3. Comparison of Over and Under-sampling Methods.srt (6.5 KB)
  • 2. Combining Over and Under-sampling - Demo.srt (6.3 KB)
  • 1. Combining Over and Under-sampling - Intro.mp4 (36.9 MB)
  • 3. Comparison of Over and Under-sampling Methods.mp4 (36.5 MB)
  • 2. Combining Over and Under-sampling - Demo.mp4 (34.3 MB)
10. Moving Forward
  • 1. Next steps.html (0.7 KB)
  • TutsNode.com.txt (0.1 KB)
    • .pad
    • 0 (0.1 KB)
    • 1 (117.5 KB)
    • 2 (982.7 KB)
    • 3 (156.1 KB)
    • 4 (428.6 KB)
    • 5 (15.5 KB)
    • 6 (93.1 KB)
    • 7 (119.9 KB)
    • 8 (965.1 KB)
    • 9 (295.3 KB)
    • 10 (1,020.1 KB)
    • 11 (945.2 KB)
    • 12 (999.7 KB)
    • 13 (989.9 KB)
    • 14 (394.6 KB)
    • 15 (496.4 KB)
    • 16 (711.3 KB)
    • 17 (272.1 KB)
    • 18 (818.2 KB)
    • 19 (397.3 KB)
    • 20 (12.8 KB)
    • 21 (132.5 KB)
    • 22 (436.7 KB)
    • 23 (511.9 KB)
    • 24 (233.8 KB)
    • 25 (764.1 KB)
    • 26 (1,014.8 KB)
    • 27 (76.9 KB)
    • 28 (99.1 KB)
    • 29 (475.2 KB)
    • 30 (823.1 KB)
    • 31 (907.3 KB)
    • 32 (681.5 KB)
    • 33 (935.9 KB)
    • 34 (714.9 KB)
    • 35 (273.0 KB)
    • 36 (582.6 KB)
    • 37 (763.1 KB)
    • 38 (406.2 KB)
    • 39 (449.8 KB)
    • 40 (562.7 KB)
    • 41 (187.5 KB)
    • 42 (466.7 KB)
    • 43 (520.8 KB)
    • 44 (287.3 KB)
    • 45 (429.8 KB)
    • 46 (249.6 KB)
    • 47 (412.7 KB)
    • 48 (834.4 KB)
    • 49 (473.7 KB)
    • 50 (683.7 KB)
    • 51 (390.0 KB)
    • 52 (416.0 KB)
    • 53 (749.4 KB)
    • 54 (833.6 KB)
    • 55 (233.8 KB)
    • 56 (240.5 KB)
    • 57 (743.2 KB)
    • 58 (16.5 KB)
    • 59 (965.2 KB)
    • 60 (987.5 KB)
    • 61 (63.9 KB)
    • 62 (98.4 KB)
    • 63 (109.7 KB)
    • 64 (354.1 KB)
    • 65 (442.4 KB)
    • 66 (574.0 KB)
    • 67 (582.7 KB)
    • 68 (753.4 KB)
    • 69 (936.1 KB)
    • 70 (51.8 KB)
    • 71 (364.8 KB)
    • 72 (783.2 KB)
    • 73 (299.8 KB)
    • 74 (309.9 KB)
    • 75 (31.9 KB)
    • 76 (41.6 KB)
    • 77 (313.7 KB)
    • 78 (630.7 KB)
    • 79 (832.1 KB)
    • 80 (938.4 KB)
    • 81 (476.0 KB)
    • 82 (843.7 KB)
    • 83 (872.3 KB)
    • 84 (749.6 KB)
    • 85 (98.7 KB)
    • 86 (362.7 KB)
    • 87 (104.1 KB)
    • 88 (39.8 KB)
    • 89 (212.6 KB)
  • [TGx]Downloaded from torrentgalaxy.to .txt (0.6 KB)

There are currently no comments. Feel free to leave one :)

Code:

  • udp://inferno.demonoid.pw:3391/announce
  • udp://tracker.openbittorrent.com:80/announce
  • udp://tracker.opentrackr.org:1337/announce
  • udp://torrent.gresille.org:80/announce
  • udp://glotorrents.pw:6969/announce
  • udp://tracker.leechers-paradise.org:6969/announce
  • udp://tracker.pirateparty.gr:6969/announce
  • udp://tracker.coppersurfer.tk:6969/announce
  • udp://ipv4.tracker.harry.lu:80/announce
  • udp://9.rarbg.to:2710/announce
  • udp://shadowshq.yi.org:6969/announce
  • udp://tracker.zer0day.to:1337/announce
REVERSE-PROXY 🔄 RP (success) | 1550ms 📄 torrent 🕐 25 Jan 2026, 12:32:44 pm IST ⏰ 19 Feb 2026, 12:32:44 pm IST ✅ Valid for 24d 23h 🔄 Wait 10m