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

Balanced datasets lead to better model performance in all cases.

Cancer is a common disease, and there are plenty of positive cases available.

Introducing more cancer cases might not reflect real-world conditions.

It enhances the complexity of the ML process.

Why is achieving a balanced dataset not recommended in scenarios like cancer detection?

Why is achieving a balanced dataset not recommended in scenarios like cancer detection?

Imbalanced datasets lead to faster training times.

Algorithms perform better on imbalanced data.

Models may become biased and perform poorly on the minority class.

Imbalanced data does not affect model performance.

Why is having an imbalanced dataset an issue for ML?

Why is having an imbalanced dataset an issue for ML?

It discards instances from the majority class.

It adds new instances only to the majority class.

It generates synthetic instances or examples for the minority class.

It removes instances from both classes to achieve balance.

How does SMOTE address class imbalance?

To decrease the number of instances in the majority class

To increase the number of instances in the minority class

Why is oversampling used in the context of imbalanced datasets?

Why is oversampling used in the context of imbalanced datasets?

In binary classification, what is the term for the class with a smaller number of examples?

In binary classification, what is the term for the class with a smaller number of examples?

It’s a visualization of the dataset used for model training.

It’s an overview of the model training process.

It’s a detailed analysis of various evaluation metrics related to the model’s performance.

It’s a breakdown of the computational resources used during training.

What is the purpose of the `classification_report`?

What is the purpose of the <code>classification_report</code>?

Which evaluation metric can be misleading when dealing with imbalanced datasets?

Which evaluation metric can be misleading when dealing with imbalanced datasets?

The model is more likely to predict the majority class accurately.


The model predicts both classes with equal accuracy.


The model assigns equal importance to both classes during training.


The model is more likely to predict the minority class accurately.

What does bias toward the majority class mean in the context of class imbalance?

What does bias toward the majority class mean in the context of class imbalance?

Test your knowledge of the fundamental concepts of imbalanced datasets and effective techniques for handling them.

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: Essential Concepts for Dealing with Imbalanced Datasets