Deal With Mislabeled And Imbalanced Machine Learning Datasets

Deal with Mislabeled and Imbalanced Machine Learning Datasets

Beginner

28 Lessons

5h

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

Explanations

This course includes

1 Project

1 Assessment

23 Playgrounds

5 Quizzes

91 Illustrations

This course includes

1 Project

1 Assessment

23 Playgrounds

5 Quizzes

91 Illustrations

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

Course Content

1

Introduction to the Course

Course Overview

Who Can Take This Course?

2

Getting Started

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

3

Understanding Noisy Data, Label Noise, and Its Types

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

4

Introduction to Convolutional Neural Network (CNN)

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

5

Performance Comparison of Mislabeled and Clean Dataset

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

6

Dealing with Imbalance Dataset

4 Lessons

Gauge the Impact of Imbalanced and Mislabeled Datasets

Project

Comprehensive Quiz

Assessment

7

Wrap Up

1 Lesson

8

Appendix

1 Lesson

COURSE AUTHORS

How You'll Learn

You don’t get better at swimming by watching others. Coding is no different. Practice as you learn with live code environments inside your browser.

Videos are holding you back. Educative‘s interactive, text-based lessons accelerate learning — no setup, downloads, or alt-tabbing required.

Learn faster and smarter with adaptive AI tools embedded in every Educative course.

Built-in assessments let you test your skills. Completion certificates let you show them off.

Recommended Courses

BEFORE STARTING THIS COURSE

AFTER FINISHING THIS COURSE

FOR TEAMS

Interested in this course for your business or team?

Unlock this course (and 1,000+ more) for your entire org with DevPath