Course Overview

In this course, we will adopt a data-centric approach to explore the impact of mislabeled data on model performance. This will help us understand how the characteristics of the data impact the performance of machine learning models. Our goal is to gain valuable insights into the relationship between data quality and the effectiveness of these models.

This course is divided into the following two sections:

In the first section, we will investigate the core ideas of machine learning. Then, we’ll explore the consequences of mislabeled data in machine learning. In the second section, we’ll examine the significance of imbalanced data in machine learning. We will also learn how to use SMOTE to deal with imbalanced data. Overall, this course will teach us about the effects of imbalanced data and the significance of correctly labeled data in machine learning models.

The process of identifying the influence of mislabeled data

The diagram below illustrates the step-by-step process of identifying the impact of mislabeled data on a machine learning model. The flowchart outlines the various stages of analysis, starting with the dataset or collection of data.

The process of identifying the impact of imbalanced data

The diagram below details a series of steps to follow in order to detect how imbalanced data can affect a machine learning model’s performance. This image provides a helpful visual guide to understanding the processes involved in detecting and addressing imbalanced data in machine learning.