Beginner
5h
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.
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
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TAKEAWAY SKILLS
Content
1.
Introduction to the Course
2 Lessons
Get familiar with handling mislabeled and imbalanced data in machine learning models.
2.
Getting Started
6 Lessons
Look at AI, ML, supervised/unsupervised learning, image classification, Python programming, and data types.
3.
Understanding Noisy Data, Label Noise, and Its Types
4 Lessons
Examine noisy data, simulate and visualize unbiased and biased mislabeling with Python.
4.
Introduction to Convolutional Neural Network (CNN)
5 Lessons
Grasp the fundamentals of CNNs, their architecture, layers, pooling, and hyperparameter tuning.
5.
Performance Comparison of Mislabeled and Clean Dataset
5 Lessons
Take a closer look at comparing CNN performance on clean vs. mislabeled datasets.
6.
Dealing with Imbalance Dataset
4 Lessons
Focus on addressing class imbalance in datasets, transforming techniques, and practical Python applications.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
Course Author:
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