Deal with Mislabeled and Imbalanced Machine Learning Datasets

This course provides hands-on experience dealing with imbalanced data in machine learning, which is critical for machine learning algorithms.

Beginner

28 Lessons

Certificate of Completion

This course provides hands-on experience dealing with imbalanced data in machine learning, which is critical for machine learning algorithms.

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

1 Project

1 Assessment

23 Playgrounds

5 Quizzes

This course includes

1 Project

1 Assessment

23 Playgrounds

5 Quizzes

Course Overview

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, ...Show More

TAKEAWAY SKILLS

Python

Machine Learning

Data Pipeline

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

What You'll Learn

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

Course Content

Introduction to the Course

This course offers a comprehensive exploration of handling mislabeled and imbalanced data in machine learning.

Course Overview

Who Can Take This Course?

Getting Started

This chapter covers the fundamentals of AI, ML, and data types, focusing on image classification and contrasting model-centric with data-centric approaches.

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

This chapter explores the effects of noisy data and mislabeling in machine learning, and simulates both unbiased and biased label noise to see the impact.

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)

This chapter covers the fundamentals of Convolutional Neural Networks which are key for effective image classification and help extract meaningful information.

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

Performance Comparison of Mislabeled and Clean Dataset

This chapter demonstrates the impact of data quality on CNN performance in image classification, emphasizing the need for accurate labeling and data cleaning.

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

This chapter shows the impact of imbalanced datasets on machine learning tasks and covers various techniques, such as SMOTE, used to address the issue.

Gauge the Impact of Imbalanced and Mislabeled Datasets

Project

Comprehensive Quiz

Assessment

Wrap Up

1 Lesson

This course provided essential skills to identify and manage imbalanced and mislabeled datasets in machine learning applications.

Appendix

1 Lesson

The appendix features key references highlighting the transition to data-centric AI and notable insights from industry experts.

Course Author

Trusted by 1.4 million developers working at companies

Anthony Walker

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Emma Bostian 🐞

@EmmaBostian

Evan Dunbar

ML Engineer

Carlos Matias La Borde

Software Developer

Souvik Kundu

Front-end Developer

Vinay Krishnaiah

Software Developer

Eric Downs

Musician/Entrepeneur

Kenan Eyvazov

DevOps Engineer

Anthony Walker

@_webarchitect_

Emma Bostian 🐞

@EmmaBostian

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