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

Impact of Mislabeling in Machine Learning.png

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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.

image

Deal with Mislabeled and Imbalanced Machine Learning Datasets

To develop intelligent machines that mimic human behavior

What is the main goal of artificial intelligence (AI)?

What is the main goal of artificial intelligence (AI)?

Supervised learning is more accurate than unsupervised learning.

Unsupervised learning is used for classification problems, while supervised learning is used for regression problems.

Supervised learning uses labeled data, while unsupervised learning uses unlabeled data.

Unsupervised learning requires explicit programming, while supervised learning does not.

What is the main difference between supervised and unsupervised learning?

What is the main difference between supervised and unsupervised learning?

```python
model.fit(train_Data, train_Labels)
```

```python
model.fit(train_Labels,test_labels)
```

```python
model.fit(train_Data, test_Data)
```

```python
model.fit(train_Data, test_labels)
```

Which command trains the machine learning model in the `sklearn` library?

Which command trains the machine learning model in the <code>sklearn</code> library?

Which option is _not_ a common image classification technique?

Which option is not a common image classification technique?

Which type of machine learning is used when the model is trained using labeled data?

Which type of machine learning is used when the model is trained using labeled data?

This command is used to import a function from the ``model_selection`` module within the `sklearn` library in order to split the input data into training and testing parts.

This command imports a function from the ``model_selection`` module within the `sklearn` library to train the model on the training data.

This command imports a function from the ``model_selection`` module within the `sklearn` library to evaluate the performance of the machine learning model.

What is the purpose of the following command?
```python 
from sklearn.model_selection import train_test_split
```

What is the purpose of the following command?
<pre><code class="language-python">from sklearn.model_selection import train_test_split
</code></pre>

Unstructured data is data that is organized in a predefined manner.

Unstructured data is data that contains patterns and structures.

Unstructured data is data that is not organized and lacks a pattern.

Unstructured data is data that can be easily analyzed with traditional computer programs.

Which statement describes unstructured data?

Which statement describes unstructured data?

Test your knowledge of the fundamentals of AI, ML, and DL.

Introduction to the Course

Getting Started

Understanding Noisy Data, Label Noise, and Its Types

Introduction to Convolutional Neural Network (CNN)

Performance Comparison of Mislabeled and Clean Dataset

Dealing with Imbalance Dataset

Gauge the Impact of Imbalanced and Mislabeled Datasets

Comprehensive Quiz

Wrap Up

Appendix

Quiz: Artificial Intelligence Basics