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

Understand the fundamental difference between the data-centric and model-centric approaches to machine learning.

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

The Model-Centric Approach vs. the Data-Centric Approach

The model-centric approach

Challenges in the model-centric approach