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

To improve the performance and reliability of ML models

What is the primary motivation for identifying and removing mislabeled instances in a dataset?

What is the primary motivation for identifying and removing mislabeled instances in a dataset?

The test accuracy improves significantly.

It depends on the mislabeling percentage.

How is the test accuracy of a model affected after training on mislabeled data compared to training on clean data?

How is the test accuracy of a model affected after training on mislabeled data compared to training on clean data?

To assess how well the model generalizes unseen data

What is the purpose of evaluating a model on the test data after training?

What is the purpose of evaluating a model on the test data after training?

A model trained on a specific dataset for a particular task

A model that has not been trained on any data

A model trained on a large and diverse dataset before being applied to a specific task

What is a pretrained model in ML?

How many channels does each image contain in the MNIST dataset?

How many channels does each image contain in the MNIST dataset?

It reduces the number of features in the data.

It speeds up model convergence and requires fewer epochs.

What is the primary advantage of normalizing data before training an ML model?

What is the primary advantage of normalizing data before training an ML model?

The true labels of the mislabeled instances

The labels assigned to the instances by a pretrained model

The accuracy for each class in the dataset

What do ``predicted_classes`` represent in code?

What do <code>predicted_classes</code> represent in code?

The model performance improves and approaches that of the original clean dataset.

The model performance becomes unpredictable.

What is the expected outcome when evaluating the model after removing mislabeled instances?

What is the expected outcome when evaluating the model after removing mislabeled instances?

Test your knowledge of model performance using clean datasets and biased or unbiased mislabelled datasets.

Introduction to the Course

Getting Started

Understanding Noisy Data, Label Noise, and Its Types

Introduction to Convolutional Neural Network (CNN)

Cats vs Dogs Classification with Convolutional Neural Networks

Performance Comparison of Mislabeled and Clean Dataset

Dealing with Imbalance Dataset

Gauge the Impact of Imbalanced and Mislabeled Datasets

Comprehensive Quiz

Wrap Up

Appendix

Dealing With Small Datasets In ML

Quiz: Evaluating the Performance of Mislabeled and Clean Datasets