Data Usage in AI Programs

What is structured data?

• Data that is organized in a fixed format and can be easily stored, accessed, and processed.

• Data without a predefined structure, such as text, images, videos, and social media posts.

• Data that has been tagged or categorized with the correct answer.

• Data that lacks explicit labels or categories.

What is unstructured data?

• Data that is organized in a fixed format and can be easily stored, accessed, and processed.

• Data without a predefined structure, such as text, images, videos, and social media posts.

• Data that has been tagged or categorized with the correct answer.

• Data that lacks explicit labels or categories.

What is labeled data?

• Data that is organized in a fixed format and can be easily stored, accessed, and processed.

• Data without a predefined structure, such as text, images, videos, and social media posts.

• Data that has been tagged or categorized with the correct answer.

• Data that lacks explicit labels or categories.

What is unlabeled data?

• Data that is organized in a fixed format and can be easily stored, accessed, and processed.

• Data without a predefined structure, such as text, images, videos, and social media posts.

• Data that has been tagged or categorized with the correct answer.

• Data that lacks explicit labels or categories.

What is the purpose of training data in machine learning?

• To evaluate the performance of the model.

• To fine-tune the model's hyperparameters.

• To provide a separate dataset for testing the model.

• To train the model to map input data to the correct output based on labeled examples.

What is the purpose of testing data in machine learning?

• To evaluate the performance of the model.

• To fine-tune the model's hyperparameters.

• To provide a separate dataset for training the model.

• To train the model to map input data to the correct output based on labeled examples.

What is the purpose of the validation process in machine learning?

• To evaluate the performance of the model.

• To fine-tune the model's hyperparameters.

• To provide a separate dataset for testing the model.

• To train the model to map input data to the correct output based on labeled examples.

What is the impact of data quality on AI outcomes?

• Biased Outcomes

• Unreliable Predictions

• Financial Losses

• All of the above