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Deal with Mislabeled and Imbalanced Machine Learning Datasets

Name: Deal with Mislabeled and Imbalanced Machine Learning Datasets
Price: 59 USD
Author: Dr. Gul Sher Baloch

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.

28 lessons

3 projects

Created by Dr. Gul Sher Baloch

Deal with Mislabeled and Imbalanced Machine Learning Datasets

What's included

28 lessons across 8 modules
5 quizzes with instant feedback
3 projects in preconfigured environments
AI Tutor on every lesson

Join 3.1 million developers at

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

Learning Roadmap

28 Lessons1 Project5 Quizzes1 Assessment

Introduction to the Course

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

Course Overview

Who Can Take This Course?

Getting Started

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.

Introduction to Convolutional Neural Network (CNN)

5 Lessons

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

Cats vs Dogs Classification with Convolutional Neural Networks

Project

Premium

Performance Comparison of Mislabeled and Clean Dataset

5 Lessons

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

Dealing with Imbalance Dataset

4 Lessons

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

Gauge the Impact of Imbalanced and Mislabeled Datasets

Project

Comprehensive Quiz

Assessment

Wrap Up

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

Conclusion

Appendix

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

References

Dealing With Small Datasets In ML

Project

Premium

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

About this course

WHY I WROTE THIS COURSE

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.

ABOUT THE AUTHOR

Dr. Gul Sher Baloch

Experienced senior data professional with 12+ years of leadership. Led 15+ successful projects, securing funding for 7+ and 200+ citations. Holds MIT AI/ML and Google data analytics certifications.

Learn more about Dr.

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