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Machine Learning With Imbalanced Data
TORRENT SUMMARY
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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
VISITOR COMMENTS (0 )
FILE LIST
Filename
Size
.pad/0
53 B
TutsNode.com.txt
63 B
[TutsNode.com] - Machine Learning with Imbalanced Data/1. Introduction/1. Introduction.srt
[TutsNode.com] - Machine Learning with Imbalanced Data/1. Introduction/7. Download Datasets.html
354 B
[TutsNode.com] - Machine Learning with Imbalanced Data/1. Introduction/8. Additional resources for Machine Learning and Python programming.html
2.6 KB
[TutsNode.com] - Machine Learning with Imbalanced Data/2. Machine Learning with Imbalanced Data Overview/1. Imbalanced classes - Introduction.srt
6.5 KB
[TutsNode.com] - Machine Learning with Imbalanced Data/2. Machine Learning with Imbalanced Data Overview/2. Nature of the imbalanced class.srt
5.9 KB
[TutsNode.com] - Machine Learning with Imbalanced Data/2. Machine Learning with Imbalanced Data Overview/3. Approaches to work with imbalanced datasets - Overview.srt
4.7 KB
[TutsNode.com] - Machine Learning with Imbalanced Data/2. Machine Learning with Imbalanced Data Overview/4. Additional Reading Resources (Optional).html
1 KB
[TutsNode.com] - Machine Learning with Imbalanced Data/3. Evaluation Metrics/10. Geometric Mean, Dominance, Index of Imbalanced Accuracy - Demo.mp4
86.8 MB
[TutsNode.com] - Machine Learning with Imbalanced Data/3. Evaluation Metrics/10. Geometric Mean, Dominance, Index of Imbalanced Accuracy - Demo.srt
12.2 KB
[TutsNode.com] - Machine Learning with Imbalanced Data/3. Evaluation Metrics/11. ROC-AUC.srt
[TutsNode.com] - Machine Learning with Imbalanced Data/9. Probability Calibration/1. Probability Calibration.srt
7.3 KB
[TutsNode.com] - Machine Learning with Imbalanced Data/9. Probability Calibration/10. Calibrating a Classifier with Cost-sensitive Learning.srt
4.6 KB
[TutsNode.com] - Machine Learning with Imbalanced Data/9. Probability Calibration/2. Probability Calibration Curves.srt
6.7 KB
[TutsNode.com] - Machine Learning with Imbalanced Data/9. Probability Calibration/3. Probability Calibration Curves - Demo.srt
11.5 KB
[TutsNode.com] - Machine Learning with Imbalanced Data/9. Probability Calibration/4. Brier Score.srt
3.7 KB
[TutsNode.com] - Machine Learning with Imbalanced Data/9. Probability Calibration/5. Brier Score - Demo.srt
8.8 KB
[TutsNode.com] - Machine Learning with Imbalanced Data/9. Probability Calibration/6. Under- and Over-sampling and Cost-sensitive learning on Probability Calibration.srt
6.2 KB
[TutsNode.com] - Machine Learning with Imbalanced Data/9. Probability Calibration/7. Calibrating a Classifier.srt
5.9 KB
[TutsNode.com] - Machine Learning with Imbalanced Data/9. Probability Calibration/8. Calibrating a Classifier - Demo.srt
7.3 KB
[TutsNode.com] - Machine Learning with Imbalanced Data/9. Probability Calibration/9. Calibrating a Classfiier after SMOTE or Under-sampling.srt
