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Deal with Mislabeled and Imbalanced Machine Learning Datasets

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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Overview

Content

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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.

Machine learning models depend thoroughly on the dataset quality they are trained on. The model’s performance deteriorates signi...Show More

WHAT YOU'LL LEARN

The ability to analyze the impact of mislabeled datasets on ML model performance

An understanding of techniques to deal with imbalanced datasets

The ability to evaluate the importance of quality data over big data

The ability to analyze the impact of mislabeled datasets on ML model performance

TAKEAWAY SKILLS

Python

Machine Learning

Data Pipeline

Content

28 Lessons1 Project5 Quizzes1 Assessment

Introduction to the Course

2 Lessons

Get familiar with handling mislabeled and imbalanced data in machine learning models.

Course Overview

Who Can Take This Course?

Getting Started

6 Lessons

Look at AI, ML, supervised/unsupervised learning, image classification, Python programming, and data types.

Overview of AI, ML, DL, and Supervised/Unsupervised Learning

Image Classification

Image Classification Using Python Programming

Data Types

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

Quiz: Artificial Intelligence Basics

Understanding Noisy Data, Label Noise, and Its Types

4 Lessons

Examine noisy data, simulate and visualize unbiased and biased mislabeling with Python.

Noisy Data and Label Noise

Simulating Unbiased Mislabeling Using Python Programming

Simulating Biased Mislabeling Using Python Programming

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

Introduction to Convolutional Neural Network (CNN)

5 Lessons

Grasp the fundamentals of CNNs, their architecture, layers, pooling, and hyperparameter tuning.

Image Classification Using Convolutional Neural Networks

Input Layer and Convolution Layer

Implementation of Pooling Layers Using Python Programming

Hyperparameter Tuning

Quiz: Fundamental Concepts of CNN Architecture

Cats vs Dogs Classification with Convolutional Neural Networks

Project

Performance Comparison of Mislabeled and Clean Dataset

5 Lessons

Take a closer look at comparing CNN performance on clean vs. mislabeled datasets.

Implementing CNNs for Image Classification in Python

Unbiased Mislabeling in Image Classification Using CNNs

Biased Mislabeling in Image Classification Using CNNs

Dealing with Mislabeled Datasets Using Pretrained Models

Quiz: Evaluating the Performance of Mislabeled and Clean Datasets

Dealing with Imbalance Dataset

4 Lessons

Focus on addressing class imbalance in datasets, transforming techniques, and practical Python applications.

Imbalanced Datasets

Methods for Transforming Imbalanced Data into Balanced Data

Dealing with Imbalanced Datasets in Python Programming

Quiz: Essential Concepts for Dealing with Imbalanced Datasets

Gauge the Impact of Imbalanced and Mislabeled Datasets

Project

Comprehensive Quiz

Assessment

Wrap Up

1 Lessons

Master the steps to tackle imbalanced and mislabeled datasets for improved data quality.

Conclusion

Appendix

1 Lessons

Get familiar with essential references on data-centric AI approaches.

References

Dealing With Small Datasets In ML

Project

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Course Author:

Dr. Gul Sher Baloch and

Hasnain Hyder

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