Gain insights into dealing with mislabeled and imbalanced machine learning datasets. Learn to analyze effects, measure and recover from noise, and interpret results to avoid bias.

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Machine learning models depend thoroughly on the dataset quality they are trained on. The model’s performance deteriorates significantly due to noisy datasets. One primary source of noise is mislabeling. Labeling is a costly, time-consuming, and error-prone stage in the machine learning pipeline. Data, if not correctly labeled, can introduce bias and inaccuracies into machine learning models.

This course offers hands-on experience in analyzing the effects of mislabeled datasets on machine learning models, especially convolutional neural networks. It emphasizes the modern data-centric perspective in machine learning. Eventually, it teaches how to measure and recover from noisy datasets.

After completing this course, you will be skilled at handling imbalanced datasets and be able to interpret results fairly to avoid bias toward minority classes. Having such skills is vital in machine learning and important for both industry and academia.

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Deal with Mislabeled and Imbalanced Machine Learning Datasets

Data that is easily interpreted by machines

What is noise in the context of data?

Errors caused by inappropriate measurement

Errors in labeling instances with incorrect classes

Which statement defines label noise?

What is the impact of mislabeling on the performance of ML models?

What is the impact of mislabeling on the performance of ML models?

**(True or False)** Mislabeling can occur when different people have different perceptions of the same data.

(True or False) Mislabeling can occur when different people have different perceptions of the same data.

It calculates the accuracy of mislabeled instances.

It determines the percentage of mislabeled instances to be mislabeled.

It generates new labels for the mislabeled images.


What is the purpose of the `mislabel_percentage` variable in biased and unbiased mislabeling?


```
mislabel_percentage = 10
```

What is the purpose of the <code>mislabel_percentage</code> variable in biased and unbiased mislabeling?
<pre><code>mislabel_percentage = 10
</code></pre>

**(True or False)** Mislabeling can occur due to insufficient labeling information.

(True or False) Mislabeling can occur due to insufficient labeling information.

Which factor can contribute to mislabeling in datasets?

Which factor can contribute to mislabeling in datasets?

What is a type of mislabeling that can happen without any intentional favoritism?

What is a type of mislabeling that can happen without any intentional favoritism?

Test your knowledge of the fundamentals of noisy data, label noise, and types of label noise.

Introduction to the Course

Getting Started

Understanding Noisy Data, Label Noise, and Its Types

Introduction to Convolutional Neural Network (CNN)

Performance Comparison of Mislabeled and Clean Dataset

Dealing with Imbalance Dataset

Gauge the Impact of Imbalanced and Mislabeled Datasets

Comprehensive Quiz

Wrap Up

Appendix

Quiz: The Fundamentals of Noisy Data, Label Noise, and Its Types