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.

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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.

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Deal with Mislabeled and Imbalanced Machine Learning Datasets

To convert the input data into a one-dimensional array

To perform normalization on the input data

To reduce the spatial size of the feature maps generated by the convolutional layers

To extract the feature map of the input image through convolution operations

What is the primary purpose of the convolutional layer in a CNN?

What is the primary purpose of the convolutional layer in a CNN?

Max pooling retains the most prominent features in the feature maps.

Average pooling provides better results for image classification.

Average pooling is more efficient computationally.

Why is max pooling often preferred over average pooling in a CNN?

Why is max pooling often preferred over average pooling in a CNN?

Matrix multiplication between the input and the kernel

Element-wise addition of the input and the kernel

Element-wise multiplication between the input and the kernel

Scalar multiplication of the input with a learnable weight

Which operation does the convolution layer perform in a CNN?

Which operation does the convolution layer perform in a CNN?

Which parameter in the convolution operation controls the movement of the filter across the input data?

Which parameter in the convolution operation controls the movement of the filter across the input data?

They work only with pixel-level information.

They automatically extract features from images.

They are not suitable for large datasets.

What is a key advantage of CNNs in terms of image classification?

What is a key advantage of CNNs in terms of image classification?

To optimize the convolutional filters for better feature extraction

To select the best activation function for the output layer

To enhance the model’s performance by optimizing different parameters

To reduce the number of layers in the network for faster training

What is the purpose of hyperparameter tuning in a CNN?

What is the purpose of hyperparameter tuning in a CNN?

The number of epochs required to achieve convergence

The number of layers in the network architecture

The number of samples processed at once during training

The number of filters used in each convolutional layer

What does the batch size represent in the context of training a CNN?

What does the batch size represent in the context of training a CNN?

The size of the training dataset used for each iteration

The number of layers in the neural network architecture

The number of times the entire training dataset is passed through the network

The learning rate used for updating the model parameters

What is the purpose of ```epochs``` during CNN training?

What is the purpose of <code>epochs</code> during CNN training?

Test your knowledge of the basics of CNN architecture.

Introduction to the Course

Getting Started

Understanding Noisy Data, Label Noise, and Its Types

Introduction to Convolutional Neural Network (CNN)

Cats vs Dogs Classification with Convolutional Neural Networks

Performance Comparison of Mislabeled and Clean Dataset

Dealing with Imbalance Dataset

Gauge the Impact of Imbalanced and Mislabeled Datasets

Comprehensive Quiz

Wrap Up

Appendix

Dealing With Small Datasets In ML

Quiz: Fundamental Concepts of CNN Architecture